# Cloud Native Auto Scaling using KEDA
Cloud Native applications require dynamic scaling to handle changing workloads efficiently. Autoscaling services are a critical component of Cloud Native architecture, ensuring that the application environment can handle variable workloads without overprovisioning resources. Autoscaling is not just about adding resources but also removing unused resources to reduce costs. Azure Kubernetes Service (AKS) is a powerful platform for running Cloud Native applications, and the Kubernetes Event-Driven Autoscaling (KEDA) project provides a scalable and event-driven approach to managing autoscaling in AKS.

#### What is KEDA?

Kubernetes Event-Driven Autoscaling (KEDA) is an open-source project designed to enable dynamic scaling of Kubernetes workloads based on incoming event traffic. KEDA works with event-driven architectures such as Azure Functions, Azure Event Hub, and Apache Kafka. It can also autoscale based on custom metrics and HTTP requests. KEDA provides a simple and scalable solution for autoscaling that is well integrated with Kubernetes.

#### Why use KEDA in AKS?

Azure Kubernetes Service provides a robust platform for running Cloud Native applications, and KEDA extends that capability by providing autoscaling services that are integrated with Kubernetes. AKS provides a secure and reliable platform for hosting Cloud Native applications, while KEDA provides a scalable and event-driven approach to managing autoscaling in AKS. KEDA allows organizations to effectively manage and scale their applications in AKS, ensuring that resources are used efficiently and costs are minimized.

#### How to use KEDA in AKS?

To use KEDA in AKS, you need to install the KEDA operator on your AKS cluster. The operator deploys the KEDA custom resource definitions (CRDs) that allow you to define scaling rules for your workloads. Once you have installed the KEDA operator, you can define scaling rules for your workloads using the KEDA ScaledObject CRD. The ScaledObject CRD specifies the target resource that needs to be autoscaled, the metrics used for scaling, and the scaling behavior. You can also define custom metrics and HTTP requests for autoscaling.

#### Benefits of using KEDA in AKS:

**Scalable**: KEDA provides a scalable and event-driven approach to managing autoscaling in AKS, ensuring that resources are used efficiently and costs are minimized. 
**Efficient**: Autoscaling services provided by KEDA are well integrated with Kubernetes and can handle variable workloads without overprovisioning resources. 
**Easy to Use:** KEDA provides a simple and scalable solution for autoscaling that is well integrated with Kubernetes and is easy to use. 
**Integrated**: KEDA is well integrated with Azure Kubernetes Service, making it an excellent choice for organizations that are already using AKS for their Cloud Native applications.

#### Conclusion:

Azure Kubernetes Service provides a powerful platform for running Cloud Native applications, and KEDA extends that capability by providing autoscaling services that are integrated with Kubernetes. KEDA allows organizations to effectively manage and scale their applications in AKS, ensuring that resources are used efficiently and costs are minimized. Technovature can help organizations leverage the power of KEDA to build Cloud Native Autoscaling services in an Azure Kubernetes environment. With our expertise in Cloud Native architecture and Azure services, we can help organizations take advantage of the full range of features and benefits provided by AKS and KEDA.

Cloud Autoscaling with KEDA

## What is Big Data?
 

Big data is a term for data sets that are so large or complex that traditional data processing applications are inadequate. Challenges include capturing data, data analysis, search, sharing, storage, security, and creating a data-driven organization.

The term "big data" generally refers to the problem of managing and analyzing large data sets. It is a broad term that can refer to a number of issues, from data management and data integration to data analysis and data visualization.

The challenges of big data include capturing data, data analysis, search, sharing, storage, security, and creating a data-driven organization.


 
### Capturing data
 

Most big data is unstructured data, meaning it doesn't fit neatly into rows and columns like the data in a traditional database. This data can come from a variety of sources, including social media, sensors, and machine-generated data.


 
### Data analysis
 

Big data analysis is the process of examining large data sets in order to uncover hidden patterns and insights. This can be done through the use of sophisticated analytical tools and techniques, or through the use of human intuition and reasoning.

Big data analysis is a critical tool for businesses and organizations that are looking to make better decisions and improve their performance. By analyzing large data sets, businesses can identify trends and patterns that would otherwise be hidden in smaller data sets. This can help businesses to make more informed decisions about their products, services, and operations.

Big data analysis can also be used to improve marketing and advertising campaigns. By understanding how people interact with digital content, businesses can create more effective and targeted campaigns.


 
## Why Big Data is Important?
 

The world is constantly changing and evolving. The way we live and work is changing and with that the way we collect and use data is changing. So what is Big Data and why is it so important?

Big Data is a term used to describe the large and ever-growing volume of data that is being generated by businesses, organizations and individuals. The term was first coined in 2008 by technology consultants at McKinsey and Company.

The explosion of data is being driven by a number of factors including:

- The growth of the internet and the number of internet users

- The growth of mobile devices and the number of mobile users

- The growth of social media and the number of social media users

- The growth of cloud computing and the number of cloud computing users

All of this data needs to be processed and analyzed to reveal patterns and trends that can help businesses make better decisions and improve their performance. This is where Big Data analytics comes in.


 
## Big Data in Business
 

 
### How can Businesses use Big Data?
 

There are a few key ways businesses can use big data to their advantage.

#### 1.&nbsp;Increased customer understanding 
Big data can be used to gain a better understanding of customers and their needs. This can be done by analyzing customer data to identify trends and patterns. This information can then be used to create targeted marketing campaigns and products that are more likely to appeal to customers.

#### 2.&nbsp;Improved decision making 
Big data can be used to help businesses make better decisions by providing them with insights into how different decisions will impact customers and the business. This can be done by analyzing data to identify correlations and patterns.

#### 3.&nbsp;Increased efficiency 
 Big data can help businesses become more efficient by helping them to identify opportunities for improvement and streamline processes. This can be done by analyzing data to identify inefficiencies and areas where improvements can be made.

#### 4.&nbsp;Improved Decision Making 
The competitive advantage of big data for businesses comes from the ability to use the data to make better decisions. Businesses have always been collecting data, but with big data, they can now collect more data, from more sources, and at a faster pace. This data can be used to improve decision-making in three ways:

- Identifying patterns and trends: Big data can be used to identify patterns and trends that would not be possible to detect with smaller data sets. This can be used to improve strategic planning, identify new opportunities, and predict future trends.

- Improving accuracy of forecasting: Big data can be used to improve the accuracy of forecasting by incorporating more data sources and using more sophisticated models.

- Enhancing decision-making processes: Big data can be used to improve the decision-making process by providing more information and faster feedback. This can help businesses make better decisions more quickly, which can give them a competitive advantage.

 
### Benefits of Big Data in Business
 

The benefits of big data for businesses are vast and varied. Perhaps the most obvious benefit is that businesses can use big data to gain a better understanding of their customers. By analyzing data from social media, surveys, website visits, and other sources, businesses can learn about what customers want and need, and how best to appeal to them. This can help businesses to improve their marketing efforts, and to create products and services that are more likely to be successful.

Big data can also be used to improve business operations. By analyzing data about how customers interact with a business's website or products, businesses can identify areas where they can make improvements. This can help to improve customer satisfaction and reduce the number of complaints and returns.

Big data can also be used to improve decision-making. By analyzing data about past sales, businesses can identify patterns and trends that can help them to make better decisions about what products to stock, how to price them, and where to market them.


 
## Big Data for Analytics
 

 
### How Big Data can be used for Analytics
 

One of the most important aspects of big data is the ability to analyze all of the data in order to find trends and patterns. The sheer size of big data sets makes it difficult to analyze them using traditional methods. However, with the help of big data analytics tools, it is possible to find trends and patterns that would otherwise be hidden in smaller data sets.

Big data analytics can be used for a variety of purposes, including marketing, risk management, and product development. For example, big data analytics can be used to determine which products are most likely to be successful based on customer data. It can also be used to identify potential security risks and to develop strategies to mitigate those risks.

Big data analytics can also be used to improve decision-making. For example, a retailer might use big data analytics to determine which products to stock in its stores. By analyzing customer data, the retailer can identify which products are most popular and which products are most likely to sell.


 
### Benefits of Big Data for Analytics
 

The benefits of using big data for analytics are vast. First, big data can help you understand your customers better. By understanding customer behavior, you can create a more effective marketing strategy. Additionally, big data can help you understand your competitors and how they are impacting your business.

Second, big data can help you improve your operations. By understanding how your business is performing, you can make changes to improve efficiency and productivity. Additionally, big data can help you identify areas where you may be losing money.

Third, big data can help you make better decisions. By understanding how different factors impact your business, you can make smarter decisions about where to allocate your resources. Additionally, big data can help you identify potential threats and opportunities.

Fourth, big data can help you improve your products and services. By understanding customer feedback, you can make changes to your products and services that will better meet customer needs. Additionally, big data can help you identify new areas for innovation.


 
## Big Data for Science
 

 
### How can big data be used for science?
 

The answer to this question is both expansive and intricate. The potential applications of big data in science are vast and diverse, spanning from improved data management and organization, to more accurate and insightful data analysis, to the development of new and innovative data-driven scientific methods.

Perhaps the most fundamental way that big data can be used for science is by improving the management and organization of scientific data. In many cases, scientists are still using spreadsheets and text files to manage and organize their data, a process that is not only inefficient, but also prone to error. By contrast, big data tools and technologies can provide scientists with powerful and sophisticated tools for managing and organizing their data. These tools can help scientists to more effectively find, access, and use their data, resulting in a more efficient and productive research process.

In addition to improving data management, big data can also be used to improve data analysis. For example, big data tools can help scientists more accurately identity patterns.


 
## Why Big Data and Why Use it ? Conclusion 
 

Big Data is a term that is used to describe the large volume of data that is being generated by businesses, organizations and individuals. The term was first used in the 1990s to describe the large volume of data that was being generated by the internet. Big Data is important because it can be used to improve business processes, make better decisions and identify new opportunities.

Big Data can be used to improve business processes by identifying trends and patterns in the data. For example, a business can use Big Data to identify which products are selling the most and identify the factors that are contributing to the success of those products. Big Data can also be used to improve customer service by identifying customer preferences and trends.

Big Data can also be used to make better decisions. For example, a business can use Big Data to identify which products are most profitable and make decisions about which products to produce based on that information.

Introduction to Big Data

### Introduction

Data architecture plays a critical role in modern-day businesses, where organizations are generating vast amounts of data from multiple sources. A cloud-native data architecture provides a scalable and cost-effective solution to handle the growing volume of data while ensuring high availability and performance.

In this article, we will discuss a cloud-native data architecture that uses Kubernetes, Kafka, Spark, and Snowflake. We will explore how these technologies can be integrated to create a highly scalable and reliable data pipeline.

Kubernetes provides a platform to deploy and manage containerized applications, while Kafka serves as a distributed streaming platform that allows you to handle high volumes of real-time data. Spark provides a fast and flexible data processing engine, while Snowflake offers a cloud-native data warehouse solution that allows you to store, analyze, and share large volumes of data.

By combining these technologies, we can build a cloud-native data architecture that is highly scalable, flexible, and fault-tolerant. In this article, we will walk through the different components of the architecture, the benefits it offers, and how you can get started with building your own cloud-native data architecture.

### Integrating Together - A Data Pipeline
Integrating Apache Kafka, Spark, PySpark, Snowflake Data Warehouse, and Distributed Kubernetes architecture can be an excellent way to create a scalable and efficient data processing pipeline. Here is a brief overview of how you can integrate these technologies for your use case:

#### Apache Kafka:
Apache Kafka is a distributed streaming platform that can be used to efficiently and reliably transfer data between different systems. You can use Kafka to ingest data from different sources, such as IoT devices, social media platforms, and log files. Kafka provides high throughput, fault-tolerance, and scalability, making it an ideal platform for handling large volumes of data.

#### Spark and PySpark:
Apache Spark is a distributed computing framework that can be used to process large volumes of data in a scalable and efficient manner. PySpark is the Python API for Spark, which allows you to write Spark applications using Python. Spark provides a set of high-level APIs for batch processing, streaming, machine learning, and graph processing. You can use Spark to process data that is ingested by Kafka and store the processed data in Snowflake.

#### Snowflake Data Warehouse:
Snowflake is a cloud-based data warehouse that provides a scalable and secure environment for storing and analyzing large volumes of data. You can use Snowflake to store the processed data from Spark and PySpark. Snowflake provides support for SQL queries, which can be used to perform complex data analysis.

#### Distributed Kubernetes architecture:
Kubernetes is an open-source platform for managing containerized applications. You can use Kubernetes to deploy and manage the various components of your data processing pipeline, such as Kafka, Spark, PySpark, and Snowflake. Kubernetes provides features such as automatic scaling, load balancing, and rolling updates, which can help ensure that your data processing pipeline is reliable and scalable.

To integrate these technologies, you can create a data processing pipeline that uses Kafka as the ingestion point, Spark and PySpark for data processing, Snowflake for data storage, and Kubernetes for deployment and management. Here is a high-level overview of how the pipeline can be structured:

**Ingest data into Kafka:** Data is ingested into Kafka from different sources using Kafka Producers.

**Process data using Spark and PySpark**: Spark and PySpark applications are deployed on Kubernetes, which can process the data ingested by Kafka in a distributed manner.

**Store processed data in Snowflake**: The processed data is stored in Snowflake, which provides a secure and scalable environment for data storage and analysis.

**Visualize data:** You can use BI tools like Tableau or Power BI to create visualizations and dashboards to analyze the data stored in Snowflake.

By integrating these technologies, you can create a scalable and efficient data processing pipeline that can handle large volumes of data.

### Data Use Case:

The use case for this data processing pipeline can vary depending on the business needs, but here is one example:

Let's say you are working for a retail company that has a large number of stores across the country. You want to track the sales data in real-time and analyze the sales trends to make data-driven decisions. The company has a point-of-sale (POS) system that generates data for each transaction.

The data processing pipeline can be used to ingest the sales data from the POS system into Kafka, which can handle the high volume and velocity of data. The data is then processed using Spark and PySpark to perform real-time analysis and generate insights. The insights can be stored in Snowflake, which provides a scalable and secure environment for data storage and analysis.

The company can use the insights generated from Snowflake to make data-driven decisions, such as optimizing inventory management, improving store layout and design, and creating targeted marketing campaigns.

With the data processing pipeline in place, the company can get a real-time view of the sales data and quickly respond to changes in the market. The pipeline can also be extended to include other data sources, such as social media data or weather data, to provide a more comprehensive view of the market trends.

Overall, the use case for this data processing pipeline is to provide real-time insights and analytics that can help businesses make data-driven decisions and stay competitive in the market.

### How can Technovature Help?

Technovature is a technology consulting firm that can help you design, develop, and deploy a data processing pipeline using Apache Kafka, Spark, PySpark, Snowflake Data Warehouse, and Distributed Kubernetes architecture.

Our team of experts can work with you to understand your business needs and design a customized solution that meets your requirements. We have experience working with these technologies and can provide guidance on best practices for designing a scalable and efficient data processing pipeline.

We can also help you with the following:

##### Architecture design:
 We can help you design a data processing pipeline architecture that meets your business needs and requirements.

##### Development: 
We can help you develop and deploy the various components of the pipeline, such as Kafka Producers, Spark and PySpark applications, and Snowflake data storage.
##### Integration:
 We can help you integrate the various components of the pipeline and ensure that the data flows smoothly between them.

##### Testing and deployment: 
We can help you test the pipeline and deploy it to a production environment.
Maintenance and support: We can provide ongoing maintenance and support for the data processing pipeline to ensure that it continues to meet your business needs.

Overall, Technovature has a solid understanding on Cloud Native and Big Data architecture and we can help you implement a scalable data architecture with a various data processing pipeline(s) that can provide real-time insights and analytics that can help your business make data-driven decisions and stay competitive in the market.







Cloud Native Data Architecture

## AI Conversational Interfaces

**Chatbots Will Be Indistinguishable From Humans by 2029**

The first conceptualization of the chatbot is attributed to Alan Turing, who asked “Can machines think?” in 1950. Since Turing, chatbot technology has improved with advances in natural language processing and machine learning.

** According to IBM - Per year 265 Billion Customer Requests are recorded. Businesses spent nearly $1.3 trillion to service these requests. Using Chatbots can help them save up to 30% of this.**

Interacting and interfacing with Traditional Internet as well as modern Digital Services has long been tied to keyboards, monitors and more lately mobile screens put together for more than forty years. The time is now for a change as we need a new intelligent and universal means of interaction that can hold voice or text conversations to link humans and machines, and to connect customers and companies.

According to a forecast by Gartner, by the end of 2020, we can see companies capturing almost 85 percent of consumer contact with the help of technology. This signifies the role of technology in customer interaction. Today, AI is not limited to only big companies. Thanks to a large number of technological innovations, any company irrespective of its size started adopting AI applications, and chatbots have been the front-runner.

#### What is a Chatbot?

*The most natural definition of a chatbot is – a developed a program that can have a discussion/conversation with a human. For example, any user could ask the bot an inquiry or a statement, and the bot will respond or perform an activity as appropriate.*

A chatbot is programmed to work independently from a human operator. It can answer questions formulated to it in natural language and respond like a real person. It provides responses based on a combination of predefined scripts and machine learning applications.

In this new wave of technology, conversation is the new interface, and chatbots are the new applications. The power of the natural language processing (NLP) and Deep Learning that process and parses human language, enable Chabots to create a simple and universal means of interacting with technology.

AI powered chatbots become more intelligent over time as they gather information and data thats then stored across their neural network. These deep learning processes is based on Natural Language Processing (NLP). NLP allows artificial intelligence (AI) bots to not only gather information, but also understand the intent of questions and human context. Think of NLP as the brain of AI bots.

By the end of the year 2019, 40 percent of communications businesses will have their customers utilizing [chatbots](https://mindmajix.com/chatbot) and 80 percent of them over the next 5 years. These numbers are due to the increasing number of people utilizing messenger applications with 2019 being an important year for them.

![chatbot-stats](/newImg/blog/aichatbot/chatbot-stats.png)

Comprehending human conversation is no easy task for a computer, and companies all over the world have invested hundreds of millions of dollars into the effort. Many of them have successfully built a series of chatbot capabilities to help chatbot agents querying and searching product knowledge extracted from a comprehensive knowledge base or from advanced analytics from the enterprise backend. 

However, many challenges still remain for consumer users using the chatbots as below.

![chatbot-challenges-2018](/newImg/blog/aichatbot/chatbot-challenges-2018.png)

### So what are the Best Practices in building a Chatbot?

#### Build a service that never sleeps

Chatbots offer a number of comparative advantages. They offer 24/7 availability, where a fully dedicated resource is providing you the desired service with unmatched control and transparency. They offer instant customer service and enhance brand value. Above all, since they are intelligent, bots can be scaled up quickly allowing you to focus on important tasks instead of being part of every conversation.

#### Empower the convenient intelligence

Using the deep learning powers, you can train chatbots to behave more like a human and deliver interactions in different languages of your choice. You can improve  customer retention rate by providing a highly customized and personalized experience to each user. Their ability to deliver a cognitive digital experience and manage multiple interactions with natural interface makes them a very cost-effective option for customer support, e-commerce sales, information retrieval, help-desk and many more such cases.

Some other key benefits to be drawn from building and deploying bots in our organization are as below:

- **They cut down Operational Costs** 💰
  Per year there are a reported 265 Billion Customer Requests. Businesses spent nearly $1.3 trillion to service these requests. Using Chatbots can help you save up to 30% of this.
- **Chatbots empowers your Team** 💪
  Chatbots will not entirely replace humans. Rather they will become the first-level support that filters a customer request and save you time.
- **They boost your Brand Outlook** 🙌
  The visual interaction offers a friendly experience and ensures a healthy rapport with your customer base spread across the world.
- **It's easier than making an App** 💁‍♀️
  In the productivity category, chatbots outperform apps in all aspects of functionality and is more cost-effective.
- **Millennials love Chatbots** 👫
  The speed of communication and ease of use has given a sweet spot for chatbots in the Millennial audience compared to legacy tools.

#### Conversational User Experience

Conversational User Experience (CUX) will be adopted by most businesses in 2019. It is a newer version of UX design, and have features like guided conversations, brand persona development, and personalized content. Its main purpose is to help businesses to create rapid and helpful customer interactions on their chatbot. CUX is something to keep in mind, as any business that fails to keep up with CUX may risk their customer support.

![NLP](/newImg/blog/aichatbot/nlp-chatbot.jpg)

(NLP) Natural Language Processing Chatbots finds a way to convert the user's speech or text into structured data.  

The goal of natural language processing (NLP) is to take the unstructured output of the ASR and produce a structured representation of the text that contains spoken language understanding(SLU) or, in the case of text input, natural language understanding (NLU).


Which is then utilized to choose a relevant answer. Natural Language Processing includes the following steps;

1.**Tokenization:** The NLP separates a series of words into tokens or pieces that are linguistically representative, with a different value in the application.

2.**Sentiment Analysis:** It will study and learn the user's experience, and transfer the inquiry to a human when necessary

3.**Normalization:** This program model processes the text to find out the typographical errors and common spelling mistakes that might alter the intended meaning of the user request.

4.**Named Entity Recognition:** The program model of chatbot looks for different categories of words, similar to the name of the particular product, the user's address or name, whichever information is required.

5.**Dependency Parsing:** The Chatbot searches for the subjects, verbs, objects, common phrases and nouns in the user's text to discover related phrases that what users want to convey.

**Consumer Domain-Specific Bots**

AI powered chatbots can be built for specific consumer domains or industry verticals such as below.

**Transportation Bots**

Instalocate provides real-time flight tracking, security wait times,ability to file for compensation for delayed flights, ability to push notifications to WhatsApp, gate and terminal information, weather updates, airport directions, baggage information, and an ability to book cabs or Uber vehicles at the airport. The bot uses rule-based heuristics for responses;conversation is restricted to system-directed initiatives. 

**Dating:**

Foxsy suggests new friends to you based on user-specified settings. Effectively a Tinder-like applications embedded within the Messenger platform. The bot uses a very basic rule-based heuristic for responses; allows for user-directed conversation, but replies with “Sorry, I didn't get it” to most queries. 

**Mediation:**

Meditate Bot provides users with information about breathing exercises and body scans and allows users to schedule daily medications. The bot notifies users when it is time for their daily medications. Peaceful Habit offers similar functionality in scheduling and timing meditation routines for its users. 

**Fitness Bots:**

GymBot allows users to keep track of the number of exercises they've completed and track their improvement over time.  FitCircle generates five-minute workouts for users. Forksy asks you about what food you're eating, and scolds you for unhealthy choices. Whole Foods bot allows you to search for healthy recipes with text or emoji requests. 

**Weather Bots:**

Poncho is a weather bot with a distinctive personality that allows users to request the current weather conditions. The Weather Channel offers a more traditional weather bot, powered by IBM Watson. Other weather bots include: HippoBot, Weatherman,and Yahoo weather; they provide similar functionality with differing personality settings.

**Medical Bots**

The symptoms defining medical conditions are often well-defined, and chatbots have the ability to improve rates of accurate self-diagnosis. One of the main problems with self-diagnosis is that most individuals do not know what questions to ask in order to diagnose a medical condition, and therefore are unable to use platforms like WebMD properly. Chatbots, which are able to ask follow-up questions until they are ready to make a diagnosis, solve that problem. Baidu,the Chinese search engine, for example, launched a diagnosis app called Melody in 2015, which responds to medical questions from users. 

### Bot Evaluation

**Evaluation Perspectives**

There are a number of different perspectives on how to evaluate chatbot performance. From an information retrieval (IR) perspective, chatbots have specific functions: there are virtual assistants, question-answer and domain-specific bots.

Evaluators should ask questions and make requests of the chatbot, evaluating effectiveness by measuring accuracy, precision, recall, and F-score relative to the correct chatbot response.

 From a user experience perspective, the goal of the bot is, arguably, to maximize user satisfaction. Evaluators should survey users (typically, measured through questionnaires on platforms such as Amazon Mechanical Turk), who will rank bots based on usability and satisfaction. 

From a linguistic perspective, bots should approximate speech, and be evaluated by linguistic experts on their ability to generate full, grammatical, and meaningful sentences. 

Finally, from an artificial intelligence (AI) perspective, the bot that appears most convincingly human (e.g. passes the Turing Test best) is the most effective.

References:

1.Chatbot: Architecture, Design & Development by Jack Cahn


AI Chatbots

## What is Apache Kafka?
 

Apache Kafka is a distributed streaming platform. It is used for handling large scale data streaming applications. Kafka is designed to handle high throughput and low latency data streaming. It is a popular choice for building real-time data pipelines and streaming applications.

Kafka is a distributed system. It is built on a cluster of nodes. The nodes in the cluster are divided in to three categories - masters, brokers and clients. The masters are responsible for managing the cluster. The brokers are responsible for handling the data streams. The clients are responsible for sending and receiving data streams.

Kafka is a message broker. It accepts messages from producers and forwards them to consumers. The messages are stored in a Kafka topic. A Kafka topic is a collection of messages. The messages in a topic are sorted in to a sequence number. The sequence number is used to order the messages.

Kafka is a partitioned message broker that helps you handle large volumes of data and speed up the distribution of messages. Kafka is designed to be scalable, fault-tolerant, and durable. It partitions messages across a cluster of servers, and helps to ensure that each partition is replicated to a minimum number of servers. This helps to ensure that messages are not lost, even if a server fails. Kafka also supports replication of messages across multiple data centers, to help ensure that messages are available even when a data center fails.

Apache Kafka is a distributed publish-subscribe messaging system that can handle large volumes of data. It is designed to be fast, scalable and durable.

Traditional messaging systems, such as IBM MQ or JMS, are based on a client-server model. The clients send messages to the server, which then forwards them to other clients. This can be inefficient for large volumes of data, as the messages have to be passed through the server each time.

Kafka is based on a distributed model, where each client publishes messages to a broker, which then distributes them to other clients. This allows for faster throughput of data. Kafka can also handle large volumes of data, as messages are not passed through the broker.

Kafka is also designed to be scalable and durable. It can handle large numbers of messages and can be deployed on multiple servers. It also uses a distributed commit log, which ensures that messages are not lost even if there is a physical hardware or a network failure.

 
## Benefits of Apache Kafka
 

Apache Kafka is a powerful streaming platform that can be used for a variety of streaming data applications. Some of the key benefits of using Apache Kafka for streaming data include:

1.&nbsp;high throughput and scalability

2.&nbsp;low latency

3.&nbsp;flexible and fault-tolerant

4.&nbsp;supports a variety of streaming data formats

5.&nbsp;Can be used for a variety of streaming data applications

 
## How do I get started with Apache Kafka
 

It's easy to get started with Apache Kafka! There are a few things you need to do in order to get set up:

### 1.&nbsp;Install Java

Kafka requires Java 8 or later. You can find installation instructions on the Java website.

### 2.&nbsp;Download Kafka

You can download Kafka here.

### 3.&nbsp;Start Kafka

Once you have Java and Kafka installed, you can start Kafka by running the following command:

$ kafka-server-start.sh config/server.properties

### 4.&nbsp;Create a Kafka topic

You can create a Kafka topic by running the following command:

$ kafka-topics.sh --create --topic my-topic --partitions 2 --replication-factor 2

### 5.&nbsp;Write to a Kafka Topic:
The "kafka-console-producer" command can be used to write data to a Kafka topic. The following example writes the string "Hello, World!" to the "test" topic:

kafka-console-producer --topic test --message "Hello, World”

Apache Kafka

*One of the things we've learned is that if you can't get it to market more quickly, there is no doubt that the market will have changed and no matter how well you've engineered it or built it or deployed it or trained your folks, it's not going to be quite right because it's just a little too late. James McGlennon Executive VP and CIO, Liberty Mutual Insurance Group*

##Enterprise Cloud Strategies for 2019-20
The adoption and evolution of Cloud technologies by (the) enterprises over the past decade has made the technology very matured and  highly potent that it compels every Enterprise IT practitioner to explore that potential and draaw some key strategies for Cloud adoption to harness the power of the Cloud in the enterprise. The key is to leverage extensive business agility, make enterprise wide Innovation really possible and a ubiquitous global access and availability to an entire suite of their enterprise IT & applications without restrictions while not compromising on the Application and IT security at the same time.

As multicloud strategies evolve, 5G, Containers and Serverless Computing influence the expectations by the Enterprise IT Practitioners and CIO/CXOs. They are

- Greatly reduced operational burden 

- Tighter alignment to costs w/usage

- Developers can/could/should be able to develop almost anything (Speed of Innovation)

- Opinionated platforms that allow for multiple use-cases

- Opinionated platforms that bake in true best of breed practices, security, scale, performance, cost aspects for you


### 1. A greater emphasis on TCO and ROI

What workloads to migrate to the cloud is very important to consider. CIOs should often ask the question as to how they can go about moving to cloud and how they can modernize apps once they are there. CIOs also ask whether moving to the cloud will cost more or less than operating on-premises environments. “There is a lot of FUD out there that that movement won't reduce costs,” Carter says. The answer depends on how CIOs architect their systems, she says, but there are also tools that can help CIOs make critical decisions.

Earlier this year, [AWS acquired TSO Logic](https://www.geekwire.com/2019/amazon-web-services-acquires-tso-logic-vancouver-startup-working-cloud-spending-analysis/) to help enterprises understand how much it will cost to shift their systems to the cloud. “What it enables you to do is look at what you have on-prem and understand that if you just took that and went straight to the cloud, you're going to spend more money,” Carter says. “Why? Because you're usually over-provisioned.”

Companies are also increasingly adopting FinOps, a combination of [business management practices and analytics software](https://www.cio.com/article/3379619/finops-best-practices-for-cloud-cost-optimization.html) that calculates the consumption of cloud once they migrate there.

### 2. Multi-cloud strategies on the rise

Enterprises are running computing workloads in production on at least two of the big three public cloud carriers. Tyson Foods is migrating applications to AWS and GCP, each of which offer unique value propositions.  While AWS has a breadth of services, GCP provides full instrumentation of its whole cloud stack per each computing node, as well as orchestration of containers for Kubernetes with some built-in AI services.

Some of the Enterprises such as State Farm is embracing both the “lift and shift” approach, which entails moving apps from on-premises systems to the cloud, and “cloud-native” development, which includes building and deploying software in the cloud, as it works with AWS, Azure and GCP, says Ashley Pettit, State Farm senior vice president of IT.

Embracing all three vendors will allow State Farm greater flexibility in developing software, says Petit, who is moving the company from mainframe and other legacy systems. State Farm is moving its Drive Safe & Save mobile application, as well as apps for pricing and underwriting models, to AWS.

![Multi-Cloud-Strategy](/newImg/blog/cloudstrategies/mcloudstrategy.png)

### 3. Serverless


Serverless has already established itself as the core backbone and the cornerstone of a Multi-Cloud and a Cloud-Native architecture (or strategy)

**Knative** is a Kubernetes-based platform to deploy and manage modern serverless workloads.

Knative components build on top of Kubernetes, abstracting away the complex details and enabling developers to focus on what matters. Built by codifying the best practices shared by successful real-world implementations, Knative solves the "boring but difficult" parts of deploying and managing cloud native services so you don't have to.

- Pluggable components let you bring your own logging and monitoring, networking, and service mesh.
- Knative is portable: run it anywhere Kubernetes runs, never worry about vendor lock-in.
- Idiomatic developer experience, supporting common patterns such as GitOps, DockerOps, ManualOps.

![Kube Layers](/newImg/blog/cloudstrategies/kube-serverless.png)

*Figure 8.1: Kubernetes Frameworks *

### 4. Innovation in the cloud

Thanks to containers, Kubernetes, and “serverless” computing, the way core enterprise applications are deployed is being reshaped. Adoption of Docker containers has grown in recent years as corporate developers have modernized application deployment.

Kubernetes, the software that automates the deployment, scaling and management of containers, has emerged as the enterprise's orchestration layer of choice. And it has become way easier to deploy, scale and secure, with vendors rolling out new features for API-driven security policy orchestration.


Serverless, in which [customers operate applications without building and maintaining the infrastructure to run them](https://www.cio.com/article/3244644/serverless-the-future-of-cloud-computing.html?nsdr=true), is already gaining traction as more companies look to roll out digital services and applications. It provides an API to associate the function with one or more event, such as when an IoT sensor facilitates an action, a file uploaded by a CRM App , too many help desk requests risen beyond a set threshold and many such scenarios.

Enterprise CIOs are trying to get out of spending time and money on maintaining systems. Moving to a serverless cloud architecture enables them to focus on what matters to their business most and increases their agility to serve their business needs for a wider collaboration and core Innovation while allowing them to develop and roll out new capabilities quickly.

**In a recent Gartner survey of technical professionals based primarily in North America, 40% of respondents indicated that their organizations would be spending the majority of new or additional funding on the cloud. However, the survey also found that the cloud is the most common area for talent gaps**

Serverless, along with containers and Kubernetes, forms the foundation for modernizing core business apps. In the very near future, we can envision that all of cloud will be serverless.  We can say that the tech industry is only at the “tip of the spear” regarding serverless. Hence nurturing core talent for adopting and maintaining a cloud-first environment or architecture becomes an imperative.

### 5. 80% of processes move to Cloud

As said above, until recently the skepticism about migration of Information Systems to Cloud was very strong. The reasons for it were a lack of confidence about the security of data and processes and the traditional resistance to change. Nowadays, data show that in the following years 80% of business processes will move to the Cloud, but not only that. Studies say that there is another trend connected to the Cloud migration. Indeed, many companies refactoring their on-premise infrastructures to move them easily to Cloud-native environments.

### 6. AI is replacing Automation in the Cloud

 It is predicted that in 2019, companies will accelerate their usage of cloud-based artificial intelligence (AI) software and services. Among companies that adopt AI technology, 70 percent will obtain AI capabilities through cloud-based enterprise software, and 65 percent will create AI applications using cloud-based development services. (source: Deloitte Global)

By 2020, penetration rates of enterprise software with integrated AI and cloud-based AI platforms will reach an estimated 87 percent and 83 percent, respectively, among companies that use AI software. Cloud will drive more full-scale AI implementations, better return on investment (ROI) from AI, and higher AI spending. Importantly, we will see the democratization of AI capabilities—and benefits—that had heretofore been the preserve only of early adopters.

Across all countries, AI early adopters are seeing positive financial returns, reporting an average ROI of 16 percent. This is a promising start for companies that are gaining experience with a rapidly evolving set of technologies with AI and also provides them a much required competitive edge in the marketplace.

Encouraged by their successes, and betting that AI will play a critical role in enhancing their competitiveness, companies are increasing their AI investments.

![AI in Cloud](/newImg/blog/cloudstrategies/AIFig.jpg)

### 7. SaaS ecosystems will rise

Integration plays are ramping up, evidenced by Salesforce.com’s purchases of API management vendor MuleSoft and data visualization player Tableau. Expect integrated SaaS-based industry ecosystems to spawn, driven by enterprise software stalwarts such as Microsoft, Oracle and SAP, Bartoletti says. Early examples will come from supply-chain-intensive industries with a strong need to share information and collaborate across organizations, such as industrial, healthcare, manufacturing and government, Bartoletti says.

### Next Steps
Technovature recommends the below next steps to kickstart a cloud-first thinking in your organization
- Start developing and implementing a cloud-first strategy now, with an emphasis on multicloud architecture. Enterprises must advance the use of public cloud services to become the primary, prioritized and promoted deployment model. 

- Embrace public cloud while continuing to evolve your data center resources to be more efficient and more hbrid. Define integration strategies between data centers and public clouds.

- Strategically rethink and invest in your network architecture for multicloud. Implement robust
network architecture that can enable hybrid IT and multicloud cost-effectively and redundantly.

- Act now to establish your organization’s cloud-first blueprint or architecture. Find the best partners who can provide with a combination of high-quality consultancy, who can also supplement with experienced enterprise architects, I&O architects or application architects specialized in the cloud-first architecture.



Cloud Strategies

### Introduction:

Autonomous driving is a rapidly growing field with tremendous potential to transform transportation. However, building an autonomous driving system involves significant challenges, such as managing large amounts of data, ensuring real-time responsiveness, and coordinating complex distributed systems. Kubernetes, a powerful open-source container orchestration platform, can help address these challenges and enable the development of more efficient and reliable autonomous driving systems.

### Problem Statement:

The development of autonomous driving systems requires the coordination of a wide range of components, such as sensors, data processing systems, and control units. These components generate massive amounts of data that need to be processed and analyzed in real-time to ensure the safe and effective operation of the autonomous vehicle. Additionally, autonomous driving systems require a highly distributed architecture to support the geographically dispersed nature of the vehicles and their interactions with the environment. This complexity creates significant challenges in terms of managing and coordinating these systems effectively.

### Solution:

Kubernetes provides a powerful solution to the challenges of building autonomous driving systems. With its container orchestration capabilities, Kubernetes can help manage and scale the various components of the system, including data processing systems, control units, and communication channels. Kubernetes can also provide a more efficient and cost-effective way to manage the system's resources, enabling better utilization of computing and storage resources across the system.

One of the key benefits of Kubernetes for autonomous driving systems is its ability to support edge computing. Edge computing involves processing data closer to where it is generated, reducing the latency and bandwidth requirements of the system. This approach is particularly important for autonomous driving systems, where real-time responsiveness is critical for safety and performance. With Kubernetes, edge computing can be easily integrated into the system architecture, allowing the system to process data closer to the source.

Kubernetes also provides a highly distributed architecture that can support the geographically dispersed nature of autonomous driving systems. With Kubernetes, different components of the system can be distributed across multiple locations, such as data centers and edge locations, while still being managed through a single control plane. This allows for better coordination and communication between the different components of the system, ensuring that the system operates effectively and safely.

### Key Benefits


Kubernetes-based Edge Computing and Cloud Native architecture can be extremely helpful in building an AI-based Autonomous Driving solution in the following ways:

**Scalability and Flexibility:** Kubernetes provides a scalable and flexible platform to deploy, manage and orchestrate containerized workloads. This makes it easier to scale up and down the resources needed for an AI-based Autonomous Driving solution based on the demand.

**Resource Management:** Edge Computing allows the processing of data to happen closer to the source, reducing latency and the need to transmit large amounts of data to a central location. This is critical for Autonomous Driving, where real-time data processing is required. Kubernetes can manage edge resources and ensure that workloads are deployed to the appropriate location.

**Fault Tolerance:** Autonomous Driving systems require high levels of reliability and fault tolerance. Kubernetes provides robust mechanisms for automated failure recovery, self-healing and rolling updates, which can ensure the system is always available and responsive.

**Automation**: Kubernetes-based Cloud Native architecture allows for automation of deployment, management, and scaling of the entire system. This reduces the need for manual intervention, minimizes errors and frees up the development team to focus on the AI algorithms.

**Data Management**: AI-based Autonomous Driving solutions require large amounts of data for training and real-time inference. Kubernetes can help manage this data through the use of distributed file systems, object stores and databases.

Overall, Kubernetes-based Edge Computing and Cloud Native architecture can provide a reliable, scalable and flexible platform to build AI-based Autonomous Driving solutions. By reducing complexity, managing resources, and automating many tasks, development teams can focus on the core AI algorithms that are essential to the solution.

### Software Components

So below we cover as to what kind of Open Source AI packages, Full Stack Observability, Microservices architecture, Edge Network using Kubernetes, IoT Sensors, Camera feed data, and Big Data architecture can be used in an Autonomous Driving solution:

**Open Source AI packages:** There are several Open Source AI packages available that can be used for Autonomous Driving solutions. TensorFlow, PyTorch, Keras, and MXNet are popular examples of AI packages that can be used to develop deep learning models for object detection, segmentation, and tracking. These packages provide pre-trained models, which can be fine-tuned for specific use cases.

**Full Stack Observability**: Observability is essential for monitoring and troubleshooting Autonomous Driving solutions. Full Stack Observability refers to a comprehensive approach to monitoring the entire stack of the solution, from the edge devices to the cloud. Tools like Prometheus, Grafana, and Jaeger can be used to provide full-stack observability, including performance monitoring, tracing, and logging.

**Microservices Architecture**: Microservices architecture provides a modular approach to building complex software systems. In an Autonomous Driving solution, microservices can be used to decouple different components, such as object detection, decision-making, and vehicle control. This allows for greater flexibility, scalability, and easier maintenance of the solution.

**Edge Network using Kubernetes:** Edge computing is critical for Autonomous Driving, as it allows for real-time processing of data and reduces latency. Kubernetes can be used to deploy containerized workloads to the edge devices, ensuring that the solution is highly available, scalable, and reliable.

**IoT Sensors and Camera Feed Data: **Autonomous Driving solutions rely on sensor data from cameras, LIDAR, radar, and other IoT devices. This data can be processed using AI models running on the edge devices to detect objects, identify obstacles, and make decisions.

**Big Data Architecture:** Autonomous Driving solutions generate large amounts of data, which must be collected, processed, and stored. Big Data architectures, such as Apache Hadoop and Apache Spark, can be used to handle and load such massive amounts of data, and perform data analytics to gain insights and improve the solution.

Some Open Source AI packages that can be used for Autonomous Driving include:

**TensorFlow:** An open-source machine learning library that can be used to build deep learning models for object detection, image segmentation, and decision-making. 
**PyTorch:** An open-source machine learning framework that can be used to build deep learning models for object detection, image segmentation, and decision-making. 
**Keras: **An open-source deep learning library that provides pre-trained models for object detection and segmentation.
MXNet: An open-source deep learning library that provides pre-trained models for object detection, segmentation, and tracking.

#### Big Data Architecture
Big Data architecture for an Autonomous Driving System that distributes Big Data processing both at the Edge using the Kubernetes platform as well as in the Cloud Core using a Cloud Native technology.

The architecture can be divided into three layers: the Edge layer, the Cloud Core layer, and the Data Management layer.

#### Edge Layer
The Edge layer consists of multiple edge devices, such as IoT sensors, cameras, and other data sources that capture data in real-time. The Kubernetes platform is used to manage these edge devices, providing a scalable and flexible environment for edge computing.

#### Data Processing
At the Edge layer, Apache Kafka can be used for data streaming and real-time processing of data from sensors and cameras. Kafka can be used to ingest data streams from different sources, process them in real time, and then store the data in the Cloud Core layer for further analysis.

Apache Spark can also be used for real-time processing at the Edge layer. Spark can process data in memory and provide low-latency data processing, making it ideal for real-time data processing.

#### Microservices Architecture
The Edge layer can also utilize a microservices architecture, where each microservice is responsible for a specific task, such as object detection, segmentation, and tracking. This approach can help reduce latency and increase the overall performance of the Autonomous Driving System.

#### Cloud Core Layer
The Cloud Core layer consists of a Cloud Native infrastructure that manages the core services of the Autonomous Driving System. This layer is responsible for handling non-real-time data processing, data analytics, and machine learning.

#### Data Processing
At the Cloud Core layer, Apache Hadoop can be used for distributed storage and processing of large datasets. Hadoop can store non-real-time data in a data warehouse system and can be used to perform batch processing, data analytics, and machine learning.

#### Microservices Architecture
The Cloud Core layer can also utilize a microservices architecture, where each microservice is responsible for a specific task, such as data analytics, machine learning, and data warehousing.

#### Data Management Layer
The Data Management layer is responsible for managing the data generated by the Autonomous Driving System. This layer includes data storage, data processing, and data analytics.

#### Data Storage
The data generated by the Autonomous Driving System is stored in different types of storage systems, such as distributed file systems, NoSQL databases, and relational databases.

#### Data Processing
The Data Management layer can also use Apache Spark for data processing. Spark can be used for data analytics, machine learning, and real-time data processing.

#### Data Analytics
For data analytics, various open-source tools can be used, such as Apache Zeppelin and Apache Superset. These tools can be used to visualize data, create dashboards, and perform ad-hoc queries.

### Real-Time versus Non-Real-Time Data Processing
The Autonomous Driving System generates both real-time and non-real-time data. Real-time data processing involves processing data in real-time as it is generated by the sensors and cameras, whereas non-real-time data processing involves processing data that has already been stored.

To handle real-time data processing, the Edge layer can use Apache Kafka and Apache Spark, while the Cloud Core layer can use Apache Spark for low-latency data processing. For non-real-time data processing, Apache Hadoop can be used for distributed storage and batch processing.

#### Event-driven Autoscaling
KEDA (Kubernetes-based Event-driven Autoscaling) can be used to automatically scale the processing resources up or down based on demand. KEDA provides the ability to monitor the event queue and scale the resources accordingly, which can help optimize the performance of the Autonomous Driving System.

### Working with Technovature

At Technovature, we specialize in helping companies navigate the complex landscape of autonomous driving technologies. We have expertise in 5G, IoT, big data, cloud-native Kubernetes, edge computing Kubernetes, and real-time operating systems, and can help integrate these technologies into a cohesive system for autonomous driving.

Our team has experience working with a wide range of clients, including automotive manufacturers, technology companies, and startups. We understand the challenges and complexities involved in developing and deploying autonomous driving systems, and we have the skills and expertise to help our clients overcome these challenges.

In addition to our technical expertise, we also offer a range of services to help our clients develop a comprehensive autonomous driving strategy, including market research, business planning, and regulatory compliance. Our goal is to help our clients develop and deploy safe and reliable autonomous driving systems that meet the needs of their customers and the requirements of regulators.

### Conclusion

Autonomous driving is a complex and rapidly evolving field that requires expertise in a wide range of technologies, including 5G, IoT, big data, cloud-native Kubernetes, edge computing Kubernetes, and real-time operating systems. At Technovature, we have the skills and expertise to help companies navigate this landscape and develop and deploy safe and reliable autonomous driving systems.

Our team of experts can help with everything from market research and business planning to technical integration and regulatory compliance. By partnering with Technovature, companies can accelerate their journey towards autonomous driving and stay ahead of the competition in this rapidly evolving field.

Driverless Autonomous Systems

## Cloud DevOps: Building a Culture of Cloud Innovation

### Introduction

Cloud computing has revolutionized the way businesses operate, enabling them to become more agile, efficient, and cost-effective. However, to fully reap the benefits of the cloud, organizations must adopt a culture of cloud innovation, which involves embracing cloud-native architectures and leveraging DevOps practices to ensure continuous delivery of software.

At Technovature, we specialize in helping businesses build and optimize their cloud environments, leveraging cutting-edge technologies such as Kubernetes, Docker, and AWS. With our expertise, we can help you achieve your cloud transformation goals, whether it's building a new cloud-native application or migrating an existing one to the cloud.

#### The Goals of a Good Cloud DevOps Strategy

A good cloud DevOps strategy should have the following goals:

### Collaboration
DevOps is all about collaboration between development and operations teams. By fostering a culture of collaboration, organizations can improve communication, reduce silos, and accelerate the delivery of software.

At Technovature, we believe that collaboration is key to successful cloud adoption. We work closely with our clients to understand their business needs and help them build a cloud environment that meets those needs.

### Automation
Automation is a critical aspect of cloud DevOps, enabling organizations to streamline processes, reduce errors, and improve efficiency. By automating tasks such as provisioning, deployment, and testing, organizations can speed up software delivery and reduce time-to-market.

At Technovature, we have extensive experience in building and implementing automation solutions using tools such as Terraform, Ansible, and Jenkins. We can help you automate your cloud environment, enabling you to focus on your core business objectives.

### Monitoring and Feedback
Monitoring is essential for ensuring the reliability and performance of cloud applications. By monitoring application performance and user behavior, organizations can gain insights into how their applications are performing and identify areas for improvement.

At Technovature, we have expertise in setting up monitoring and feedback mechanisms using tools such as Prometheus, Grafana, and ELK stack. We can help you set up monitoring solutions that provide real-time visibility into your cloud environment, enabling you to make data-driven decisions.

###  Continuous Improvement
Continuous improvement is a core principle of DevOps, which involves constantly refining and optimizing processes to improve efficiency and reduce waste. By adopting a culture of continuous improvement, organizations can drive innovation and stay ahead of the competition.

At Technovature, we believe in continuously improving our own processes and methodologies to ensure we deliver the best possible results for our clients. We leverage our expertise in cloud technologies to help our clients identify opportunities for optimization and improvement.

### Benefits of Cloud DevOps

Adopting a cloud DevOps strategy can bring a range of benefits to organizations, including:

1. Faster Time-to-Market
By adopting DevOps practices, organizations can accelerate the delivery of software, reducing time-to-market and enabling them to respond quickly to changing market conditions.

2. Increased Efficiency and Cost Savings
Automation and streamlining processes can reduce errors and increase efficiency, leading to cost savings and a more optimized cloud environment.

3. Improved Collaboration
DevOps fosters collaboration between development and operations teams, enabling better communication, reduced silos, and faster problem resolution.

4. Enhanced Reliability and Scalability
Monitoring and feedback mechanisms enable organizations to identify and address issues before they impact users, improving application reliability. Automation and cloud-native architectures also enable organizations to scale their applications quickly and easily.

### Conclusion

At Technovature, we understand the importance of cloud innovation and DevOps in driving business success. By adopting a cloud DevOps strategy, organizations can accelerate software delivery, reduce costs,

Cloud Innovation Delivered

Modern JamStack Websites

With technical advancements the application development and management has moved to the
cloud infrastructure. And now this is the era of the advanced cloud-native apps with more and
more container adoption. **Kubernetes**, aka **K8s**, is the orchestration framework of those
containers.

Leveraging the competitive advantages of multiple application development methodologies,
Cloud Native Computing, the new buzz word is taking the IT industry by storms. The
umbrella technology for all the tools and techniques for application developers, Cloud Native
Computing has emerged as the first choice for all the professionals.

The new technology and its container orchestration with **Kubernetes** need more elaboration,
and surveying its landscape the pros and cons needs to be weighed as well. So we have come
up with a complete informational article for your perusal.

Let’s delve into the details!

 
## What is Cloud Native Computing?
 

When one asks the professionals in the domain, one is sure to get a potpourri of
replies! To clarify the concept and get exposure let’s start with a simplistic definition.

Cloud native computing is an approach to design, code, and operate the applications
workloads being built in the cloud while enjoying the complete vantage of cloud model.

Next comes the official definition by Cloud Native Computing Foundation which can be
explained in other words as:

Cloud Native Computing enables the organizations to develop and run the highly scalable
applications across multiple dynamic environments and clouds. This approach is exemplified
by containers, service meshes, immutable infrastructures, and declarative APIs.

These techniques, in unison, empower the loosely coupled systems and the robust automation
enables the developers make high impact changes easily and frequently with minimum
labour.

In this ultra-dynamic era, users are highly demanding and as a result business systems
are getting more and more complex with each passing day. To keep up the pace with, they are
required to enhance the capabilities for the strategic transformation. Speeding the business
growth, rapid responsiveness, creative features, getting new ideas with the least TTM and
zero downtime are the major desideratum.

And cloud native computing is all about this speed, agility, and the resilience!

 
## How cloud native computing is different from cloud computing?
 

Though often confused, there is a humungous different in both aspects.

Cloud computing is the on-demand delivery of the digital infrastructure like servers, data
storage, databases, and application services over internet. In general, they are offered by a
cloud platform like AWS, MS-Azure, Google Cloud with a price only for what you use.

On the other hand, cloud native is the architecture for assembling all aforementioned cloud
components in the most optimized manner for the cloud environment. It is about the services-
not the servers involved, as in the cloud computing.

 
## Pillars of Cloud Native Computing
 

Precisely there are six main pillars of Cloud-Native Technology

1.&nbsp;Cloud Infrastructure

2.&nbsp;Modern Designs

3.&nbsp;Micro Services

4.&nbsp;Containers- **Kubernetes &amp; Kubernetes Dockers**

5.&nbsp;Backing Services

6.&nbsp;Automation

 
## Cloud Native Landscape
 

In Cloud-native, there are four important layers to comprehend:

### 1. Development Layer

The first layer of the stack, also known as the app definition layer, is about the developer
tools required in building application database, related systems and CI/CD pipelines.

### 2. Provisioning Layer

The next layer is about the associated things which are required to develop the app and secure
the environment where the app is deliberated to be deployed. This generally involves
assuming infrastructure as a code, automating development, and addressing the security
threats with vulnerability scans.

### 3. Runtime Layer

The third layer has to do with all the things required in running a cloud native application.
Container runtimes, **dockers**, storage, networking and many more comes in purview of this
layer.

### 4. Orchestration and Management Layer
In the last layer all the tools required to deploy, manage, scale, orchestrate and schedule the
containerized application are brought together.

To get a better glimpse of the landscape, Cloud Native Computing Foundation has developed
a highly interactive and amazingly interesting graphic of the ecosystem.

This can be accessed by [clicking here!](https://landscape.cncf.io/)

 
## Leading Edges of Cloud Native Technology
 

The technology or even the application being developed with it has major advantages.
Considerable ones include:

### 1. Code Script Flexibility

In the on-premise technologies, the codes are written in conventional languages while the
cloud-native apps are mostly written in web-friendly languages. The flexibility of working
with any scripts is better for the enterprises as it can help them get exceptional talents for any
development task.

### 2. Easy Updates

Using the ‘waterfall methodology’ the on-premise apps are updated once in a year or even
biannually. This is also for the reason for the hardships being faced in the updates. While the
cloud-native allows one to easily update the highly available and resilient apps with the
utmost ease. This advantage boosts the productivity and creativity of the developers and helps
them add more features- more frequently.

### 3. Multitenant Model

Other major productivity boost to the developer teams is the multitenancy support in the
cloud native model. The apps seamlessly perform within the virtualized space and easily
share the resources with other apps as well.

### 4. Demand-Consumption Flexibility

Where the on-premise apps requires additional infrastructure provisioning to scale operations
effectively, the cloud native apps alters the consumption as per the demands. This also
translates into the cost-effectiveness as the cloud model asks to pay what you have consumed.

### 5. Automation

Cloud Native Technology paves path to more robust automation. This allows developers to
code once and then focus the attention to other tasks, the rest will be managed by the
automation tools.

### 6. Data Carrying between different sessions

Cloud native applications do not carry data from one session to the other. This translates into
easier scalability among different servers, easy cache for boosted performance, use lesser
storage, and avoid vendor-lock-in.

 
## Challenges Associated
 

Shifting the on-premise applications to the cloud-native environment without making changes
in the architecture might prove to be a blunder! But making those changes is also an
engineering challenge in itself.

The organizations going to cloud native model encounter the following main problems:

* Finding the right skill mix
* Cloud-Centric Security Model
* Management of changing cost profile

 
## In a Nutshell
 

Finding the most optimized solution to the aforementioned glitches, the developers and the
organizations must try to embrace the cloud native environment- the future of the IT
technologies.

Either this can be made possible by developing new apps for supporting the cloud
environment or by dissolving the large applications into the micro-services, which are better
suited to the environment.

The advanced agile development principles supported by the cloud native environment not
only saves time and efforts but also enhances the cost-effectiveness. The related managed
containerization services are offered by **AWS Kubernetes** and **Azure Kubernetes**.

The advanced automation features and flexibilities associated will help the organizations
easily outsmart and outperform the competition- paving the path for an overarching
amelioration!

Cloud Native Computing

# Cloud FinOps: Optimizing Cloud Costs with Technovature

Cloud computing has revolutionized the way businesses operate by providing instant access to computing resources on demand, making it easier to scale and manage workloads. However, as businesses grow and expand their cloud usage, cloud costs can quickly become a significant expense. This is where cloud financial operations (FinOps) come in - a practice of optimizing cloud costs while maintaining cloud performance and availability.

At Technovature, we specialize in helping businesses optimize their cloud costs through a combination of cutting-edge technology and best practices. In this article, we will explore the world of Cloud FinOps and how Technovature can help your business maximize your cloud investment.

### The Problem with Cloud Costs

As cloud usage continues to increase, businesses often struggle to keep up with the costs associated with running their workloads in the cloud. This can lead to overspending, unoptimized usage, and lack of visibility into cloud costs. According to a report by Flexera, 60% of businesses have exceeded their cloud budgets, with 35% of cloud spend wasted on resources that are not being used.

Without proper management, cloud costs can quickly spiral out of control, impacting the bottom line of your business. This is where Cloud FinOps comes in - a set of practices and processes designed to optimize cloud costs while maintaining performance and availability.

### The Solution: Cloud FinOps with Technovature

At Technovature, we believe that Cloud FinOps is a crucial component of any cloud strategy. Our team of experts can help your business implement a Cloud FinOps framework to reduce costs and maximize the value of your cloud investment.

Our approach to Cloud FinOps includes:

### Cloud Cost Management
Technovature can help your business gain visibility into cloud costs and implement cost management best practices. This includes identifying and eliminating unused or underutilized resources, optimizing resource allocation, and establishing cost allocation and chargeback models.

### Performance Optimization
Our team of experts can help your business optimize your cloud infrastructure to ensure maximum performance and availability while reducing costs. This includes implementing auto-scaling, load balancing, and leveraging cloud-native services to reduce costs.

### Continuous Monitoring and Optimization
Technovature can help your business monitor and optimize cloud usage and costs continuously. We use industry-leading tools and technologies to provide real-time visibility into cloud costs and usage patterns, enabling proactive cost optimization.

### Cloud Governance
Our team can help your business establish cloud governance policies and procedures to ensure cloud usage is aligned with business objectives and meets regulatory requirements.

### Cost Optimization Training
At Technovature, we believe that proper training and education are essential for effective Cloud FinOps. We offer training and education to your team, empowering them to identify and optimize cloud costs continuously.

### The Benefits of Cloud FinOps with Technovature

Implementing Cloud FinOps with Technovature can provide several benefits to your business, including:

**Cost Optimization:** Our approach to Cloud FinOps can help your business optimize cloud costs by identifying unused or underutilized resources, implementing cost allocation and chargeback models, and leveraging cloud-native services.

**Performance Optimization**: We can help your business optimize cloud infrastructure to ensure maximum performance and availability while reducing costs. This includes implementing auto-scaling, load balancing, and leveraging cloud-native services.

**Continuous Monitoring** and Optimization: Our team can help your business monitor and optimize cloud usage and costs continuously, providing real-time visibility into cloud costs and usage patterns.

**Cloud Governance**: Technovature can help your business establish cloud governance policies and procedures to ensure cloud usage is aligned.

### Conclusion
In conclusion, implementing a Cloud Finops strategy is essential for organizations looking to optimize their cloud computing costs. By following the best practices outlined in this article and leveraging the latest tools and technologies, businesses can gain complete visibility and control over their cloud spending. This will not only help reduce costs but also enable organizations to make data-driven decisions and drive innovation.

At Technovature, we specialize in delivering cutting-edge cloud solutions and helping our clients build a culture of cloud innovation. Our team of experts can help you design and implement a Cloud Finops strategy that aligns with your business goals and objectives. Contact us today to learn more about how we can help you optimize your cloud computing costs and drive innovation with the latest cloud technologies.


Cloud Finops

# Workload Identity Federation using SPIFFE/SPIRE and Azure AKS

## Introduction

As organizations continue to adopt cloud-native technologies, the complexity of managing identity and access for their applications and services also increases. Workload Identity Federation is a promising approach that addresses this challenge by providing a secure and seamless way to manage identity and access for applications and services running in Kubernetes clusters. In this article, we will explore how to implement Workload Identity Federation using SPIFFE/SPIRE and Azure AKS.

## Background

In traditional enterprise environments, applications and services are often deployed on-premises or in a private data center. Identity and access management (IAM) is typically managed using centralized directory services such as Microsoft Active Directory, LDAP, or OpenLDAP. Applications and services authenticate and authorize users by querying these directory services.

In contrast, cloud-native environments such as Kubernetes clusters are highly distributed, dynamic, and elastic. Applications and services are typically deployed in containers and orchestrated by Kubernetes. IAM in Kubernetes clusters is typically managed using service accounts, which are Kubernetes objects that provide an identity for pods and containers running in the cluster.

Workload Identity Federation enables Kubernetes clusters to securely authenticate and authorize with other Kubernetes clusters or external identity providers using their respective service accounts. This approach eliminates the need to manage IAM using centralized directory services.

### Use case

Let's consider a use case where an e-commerce company has multiple Kubernetes clusters in different regions, each hosting a different set of microservices. The company wants to implement a secure and seamless way to manage identity and access for these microservices across different clusters.

To achieve this, the company decides to implement Workload Identity Federation using SPIFFE/SPIRE and Azure AKS. The solution involves creating a trust domain for each cluster and configuring SPIRE agents to issue SVIDs (SPIFFE Verifiable Identity Documents) for each service account in each cluster. The SVIDs are used to authenticate and authorize requests between the microservices in different clusters.

With Workload Identity Federation, the microservices in each cluster can securely access microservices in other clusters without the need to manage IAM using centralized directory services. This approach provides a seamless and secure way to manage identity and access across multiple Kubernetes clusters.

### Benefits to business

Implementing Workload Identity Federation using SPIFFE/SPIRE and Azure AKS provides several benefits to the business, including:

**Improved security**: Workload Identity Federation eliminates the need to manage IAM using centralized directory services, which can be a single point of failure and a security risk. By using service accounts and SVIDs, the solution provides a secure and seamless way to manage identity and access for microservices across different clusters.

**Increased agility**: Workload Identity Federation enables the company to easily manage identity and access for microservices across different clusters, which can help increase agility and accelerate development.

**Cost savings:** By eliminating the need to manage IAM using centralized directory services, the company can save on infrastructure and licensing costs.

### Conclusion

Workload Identity Federation using SPIFFE/SPIRE and Azure AKS provides a secure and seamless way to manage identity and access for microservices across different Kubernetes clusters. This approach eliminates the need to manage IAM using centralized directory services, improves security, increases agility, and can result in cost savings. 

Technovature can help organizations implement Workload Identity Federation using SPIFFE/SPIRE and Azure AKS, providing expertise and best practices to ensure a successful implementation.

Cloud Native Workload Identity Federation


# Kubernetes Edge Computing: A Guide to Optimizing IoT and Big Data Processing with Technovature

As the world becomes increasingly connected, IoT devices and the data they generate are exploding. The need for efficient, reliable processing of this data has led to the rise of edge computing. Edge computing is a distributed computing paradigm that brings computation and data storage closer to the devices and sensors that produce it, reducing latency and improving data processing speed.

Kubernetes, the popular container orchestration system, has emerged as a leading solution for deploying and managing edge computing workloads. With its ability to manage and scale containerized applications across a distributed infrastructure, Kubernetes enables organizations to optimize IoT and Big Data processing at the edge.

At Technovature, we specialize in helping organizations implement Kubernetes-based edge computing solutions. Our team of experts has a deep understanding of the unique challenges and opportunities presented by edge computing, as well as the technical expertise required to design, deploy, and manage Kubernetes clusters optimized for edge workloads.

### The Benefits of Kubernetes for Edge Computing

By leveraging Kubernetes for edge computing, organizations can realize a number of important benefits:

#### Improved Performance and Latency Reduction
By processing data closer to the source, edge computing reduces the latency associated with transmitting data to centralized data centers for processing. This enables organizations to more quickly and efficiently process IoT and Big Data workloads, improving overall performance and reducing the risk of bottlenecks and other performance issues.

#### Greater Flexibility and Scalability
Kubernetes provides a flexible and scalable platform for deploying and managing edge computing workloads. With its ability to manage and scale containerized applications across a distributed infrastructure, Kubernetes enables organizations to easily adapt to changing workload demands and scale resources up or down as needed.

#### Increased Security
With its built-in security features, Kubernetes provides a secure platform for processing sensitive data at the edge. By implementing security policies and controls within Kubernetes, organizations can ensure that their edge computing workloads are protected from cyber threats and other security risks.

### Key Considerations for Kubernetes Edge Computing

While Kubernetes offers a number of advantages for edge computing, there are several key considerations organizations must take into account when designing and deploying Kubernetes-based edge computing solutions:

1. Network Connectivity
Edge computing relies on a reliable and high-speed network connection between edge devices and data centers. Organizations must carefully consider network connectivity when designing Kubernetes clusters for edge workloads, ensuring that they can reliably process data and communicate with other nodes in the cluster.

2. Resource Constraints
Edge devices typically have limited resources, including processing power, memory, and storage. Organizations must optimize their Kubernetes clusters for these constraints, ensuring that workloads are deployed in a way that minimizes resource utilization and maximizes efficiency.

3. Security and Compliance
Processing sensitive data at the edge requires careful attention to security and compliance. Organizations must implement robust security policies and controls within their Kubernetes clusters, ensuring that data is protected from cyber threats and other security risks.

At Technovature, we understand the unique challenges and considerations involved in designing and deploying Kubernetes-based edge computing solutions. With our deep expertise in Kubernetes, service mesh technologies like Istio and Linkerd, and edge computing architectures like KubeEdge and OpenNESS, we can help organizations design and deploy secure, reliable, and efficient Kubernetes clusters optimized for edge workloads.

### Conclusion

Kubernetes Edge Computing is a powerful tool for businesses looking to take advantage of the growing demand for low-latency, high-bandwidth applications. It allows for the efficient deployment and management of applications at the edge, enabling businesses to provide better services to their customers. However, implementing Kubernetes Edge Computing can be complex and requires a high level of expertise in Kubernetes, networking, and edge computing technologies.

At Technovature, we have extensive experience in designing and implementing Kubernetes-based solutions for businesses of all sizes. We can help you navigate the complexities of Kubernetes Edge Computing, from selecting the right hardware and networking solutions to designing and deploying applications at the edge. With our expertise, you can ensure that your business is able to take full advantage of this powerful technology and stay ahead of the competition. Contact us today to learn more about our Kubernetes Edge Computing solutions and how we can help your business thrive in the era of edge computing.

Kubernetes Development | Technovature

## Elasticsearch Use Case for Scientific and Research Industry: Large Scale Data Analysis

The scientific and research industry is producing vast amounts of data from experiments, simulations, and observations. This data needs to be analyzed to extract meaningful insights, but the volume and complexity of the data can make this a challenging task. Elasticsearch is a powerful search and analytics engine that can help to overcome these challenges and enable researchers to make new discoveries.

One use case for Elasticsearch in the scientific and research industry is in large scale data analysis. With Elasticsearch, researchers can quickly search and analyze large datasets, identify patterns, and extract insights. Here are some key features of Elasticsearch that make it well-suited for this use case:

**Scalability**: Elasticsearch is designed to be highly scalable, with the ability to handle large amounts of data and users. Researchers can easily scale up or down their Elasticsearch clusters based on their needs, making it ideal for large-scale data analysis.

**Real-time search and analytics**: Elasticsearch can process and search data in real-time, enabling researchers to quickly analyze and identify patterns in their data. This can be particularly useful in situations where real-time analysis is critical, such as in medical research.

**Full-text search: **Elasticsearch provides powerful full-text search capabilities, making it easy for researchers to search through large volumes of text data, such as scientific papers or patient records.

**Data aggregation and visualization:** Elasticsearch can be used to aggregate and visualize data, allowing researchers to quickly gain insights and identify patterns in their data.

### Case Study: Large Cluster of Elasticsearch Nodes for Particle Physics Research

One example of Elasticsearch being used in the scientific and research industry is in the field of particle physics. The European Organization for Nuclear Research (CERN) uses Elasticsearch to search and analyze data from the Large Hadron Collider (LHC), the world's largest and most powerful particle accelerator.

The LHC generates vast amounts of data, and CERN needed a solution that could handle this data and provide real-time search and analysis capabilities. To meet this need, CERN implemented a large cluster of Elasticsearch nodes, with each node containing up to 1.2 terabytes of data.

The Elasticsearch cluster is used to search and analyze data from the LHC, enabling researchers to identify new particles and analyze the properties of existing ones. The cluster also provides real-time monitoring and analysis of the LHC's performance, allowing CERN to quickly identify and address any issues.

### Conclusion

In conclusion, Elasticsearch is a powerful tool for large-scale data analysis in the scientific and research industry. Its scalability, real-time search and analytics, full-text search capabilities, and data aggregation and visualization features make it well-suited for handling large amounts of data and extracting insights from it. The use case of Elasticsearch in particle physics research at CERN demonstrates its potential for helping researchers make new discoveries and advancing our understanding of the world around us.

At Technovature, we specialize in building Elasticsearch clusters and customizing them to meet the unique needs of our clients in the scientific and research industry. Contact us today to learn more about how we can help you unlock the full potential of Elasticsearch for your organization.

Peta-Scale Search using Elasticsearch

## How Businesses use and manage Big Data and their benefits
 

 
### Applications of Big Data
 

Big data is not just a big buzzword anymore. It is a big reality. The volume, variety and velocity of data is growing faster than businesses can keep up. The challenge for businesses is to find ways to use this data to their advantage.

One way to use big data is to gain a competitive advantage. By understanding customers and their needs, businesses can create products and services that appeal to them. They can also target them with advertising that is more relevant to them.

Another way to use big data is to improve operations. By analyzing data, businesses can find ways to streamline processes and make them more efficient. This can lead to lower costs and improved customer service.

Big data can also be used to make better decisions. By analyzing past data, businesses can make predictions about the future. This can help them make informed decisions about where to allocate resources and how to respond to changes in the market.

The bottom line for big data is that it can provide an organization with valuable insights that can help them make better decisions. By analyzing large data sets, businesses can identify trends and patterns that they would not be able to see if they were looking at smaller data sets. This can help them to optimize their operations, identify new market opportunities, and improve their customer service.


 
## Different ways Businesses can use Big Data to improve their operations
 

 
### Trends in big data
 

As big data becomes more prevalent in business, it is important to understand the different ways it can be used to improve operations. Here are three trends in big data for businesses:

#### 1.&nbsp;Using big data to improve customer service

Big data can be used to improve customer service in a number of ways. For example, businesses can use big data to track customer behavior and preferences. This can help businesses better understand what products and services their customers are likely to want and need. Additionally, big data can be used to track customer complaints and feedback. This can help businesses identify and address customer service issues before they become bigger problems.

####2.&nbsp;Using big data to improve product development

Big data can also be used to improve product development. For example, businesses can use big data to track customer feedback on current products and services. This can help businesses identify areas where they need to make changes or improvements. Additionally, big data can be used to track competitor products and gain valuable insights into their competition standing.


 
## What Innovations Businesses can do? 
 

 
### Future of Big Data
 

There are many innovative things businesses can do for the future of big data. One way businesses can use big data is by predictive analytics. Predictive analytics can help businesses better understand their customers and what they might want in the future. Additionally, predictive analytics can help businesses improve their marketing strategies and better understand their competition.

Another way businesses can use big data is by data mining. Data mining can help businesses find new and innovative ways to improve their products and services. Additionally, data mining can help businesses find new customers and understand their current customers better.

Businesses can also use big data to improve their website. Big data can help businesses better understand how people are using their website and what they are looking for on the website. Additionally, big data can help businesses track their website’s performance and find ways to improve it.

Businesses can also use big data to improve their operations. Big data can help businesses better understand how their employees are working.


 
## Predictions for how big data will continue to evolve and what implications this will have for businesses
 

Big data is still evolving and businesses need to pay attention to the changes in order to stay ahead of the competition. Here are four predictions for how big data will continue to evolve:

###1.&nbsp;The continued growth of big data will lead to more specialization

As big data becomes more complex, businesses will need to specialize in order to effectively use and analyze it. For example, businesses will need to hire data scientists who are experts in analyzing big data and using it to make informed decisions.

###2.&nbsp;The use of big data will become more widespread

Big data is no longer just for large businesses. Small businesses are starting to realize the value of big data and are using it to improve their operations.

###3.&nbsp;The use of big data will lead to more innovation

Big Data can be used to improve any aspect of a business. As businesses become more familiar with Big Data, they will find innovative and new ways to improve business efficiencies, business processes etc.

How does Big Data impact Businesses

# How adopting Kubernetes for business can increase efficiency and save money

### Kubernetes is a key technology in the cloud-native ecosystem.

### What is Kubernetes?

Kubernetes is an open-source container orchestration platform that automates the deployment, scaling, and management of containerized applications. It was originally developed by Google and is now maintained by the Cloud Native Computing Foundation (CNCF). Kubernetes provides a robust set of features that make it easy to manage containers at scale, including automated container deployment, scaling, and orchestration, as well as automated rollouts and rollbacks, self-healing, and automated scaling.

### What does it mean to be cloud-native?

Being cloud-native means developing applications that are optimised for the cloud environment. Cloud-native applications are designed to be scalable, resilient, and flexible, and they take advantage of cloud technologies such as containers, microservices, and serverless architectures. These applications are designed to run in a cloud environment, whether that's a public cloud, a private cloud, or a hybrid cloud.

To be cloud-native with Kubernetes, developers must take advantage of Kubernetes' features to design and deploy applications that are optimised for the cloud. Kubernetes provides a number of key features that enable cloud-native development, including:

**Containerization**: Kubernetes makes it easy to deploy and manage containerized applications, which are lightweight and portable.

**Scalability**: Kubernetes enables automatic scaling of containers to meet changing demand, ensuring that applications can handle traffic spikes without downtime.

**Resiliency**: Kubernetes provides self-healing capabilities that ensure that applications can recover from failures without manual intervention.

**Flexibility**: Kubernetes enables flexible deployment options, including on-premises, in the cloud, or in a hybrid environment.

**Automation**: Kubernetes automates many of the tasks associated with application deployment, scaling, and management, reducing the burden on developers and IT operations teams.


#### Benefits for businesses

Certainly, there are many benefits for businesses in adopting a cloud-native approach with Kubernetes. Here are some of the key advantages:

**Faster Time to Market**: With Kubernetes' automation capabilities and cloud-native architecture, businesses can develop and deploy applications faster, enabling them to bring new products and services to market more quickly.

**Greater Scalability**: Kubernetes enables automatic scaling of containers to meet changing demand, ensuring that businesses can handle traffic spikes without downtime. This can be especially beneficial for businesses with unpredictable or seasonal demand.

**Increased Resilience**: Kubernetes' self-healing capabilities ensure that applications can recover from failures without manual intervention, reducing the risk of downtime and improving application reliability.

**Improved Efficiency**: By automating many of the tasks associated with application deployment, scaling, and management, Kubernetes can free up IT operations teams to focus on other areas of the business.

**Lower Costs**: Kubernetes' containerization and automation capabilities can help businesses reduce infrastructure costs by optimising resource utilisation and minimising downtime.

In summary, adopting a cloud-native approach with Kubernetes can help businesses to innovate faster, scale more efficiently, and reduce costs, ultimately enabling them to compete more effectively in today's fast-paced digital landscape.

#### How to build cloud-native applications with Kubernetes?

To build cloud-native applications with Kubernetes, developers should follow a few key principles:

**Use containerization**: Develop applications using containers to ensure that they are lightweight and portable.

**Use microservices:** Break applications down into small, independent services to enable greater flexibility and resilience.

**Use automation**: Use Kubernetes' automation capabilities to automate deployment, scaling, and management tasks.

**Use cloud-native services**: Take advantage of cloud-native services such as serverless computing and managed databases to simplify application development and deployment.

**Use continuous delivery**: Implement continuous delivery practices to enable rapid iteration and deployment of new features and bug fixes.

#### Conclusion
Kubernetes is a powerful tool for building cloud-native applications. By leveraging Kubernetes' features, developers can design and deploy applications that are scalable, resilient, and flexible. Cloud-native applications are designed to run in a cloud environment, taking advantage of cloud technologies such as containers, microservices, and serverless architectures. 

With Kubernetes, developers can automate many of the tasks associated with application deployment, scaling, and management, enabling faster development cycles and more frequent deployments.




Adopting Kubernetes for Business 

![blokchain02](/newImg/blog/blockchain/blokchain02.jpg)

## What is blockchain? 
A blockchain is a distributed, digital ledger. The ledger records transactions in a series of blocks. It exists in multiple copies spread over multiple computers, typically known as nodes.

Now, let's compare with supply chain. The key blockchain features of Bitcoin align with the basic needs for reliability and integrity in a supply chain. 

**The ledger is secure because each new block of transactions links back to previous blocks in a way that makes tampering practically impossible. **

Because it is decentralized, the blockchain ledger does not depend on any single entity (like a bank) for safekeeping. The nodes connected to the blockchain network get updated versions of the ledger every time a new transaction takes place.

The multiple copies of the ledger are the “truth” about every transaction made so far in the blockchain. Any attempt at falsification would mean having to tamper with all the copies at precisely the same moment. The chances of being able to do this in blockchain networks of any useful size are negligible.

###**Consensus**
All the entities in the chain agree that each transaction is valid. For Bitcoin, that means a transfer of an amount of Bitcoin. For supply chain, it could be payment, warehousing, [transport or delivery](https://www.logisticsbureau.com/transport-consultants-fleet-management-design-ad/).

###**Provenance**
The entities in the chain know where each asset originated. They also know who owned it before and at what time. For Bitcoin, the asset is money. For supply chain, assets can be anything from iron ore and wheat to cash, machines, and copyrights.

###**Immutability**
No entity can tamper with an entry in the distributed ledger. It is not possible to erase a Bitcoin transaction. Only a new Bitcoin transaction can reverse the effect of a previous one. Similarly, with blockchain, it would not be possible to falsify a supply-chain payment transaction or the records of inventory, warehousing conditions, delivery times and dates, and so on.

###**Finality**
The copies of the shared ledger all hold the same version of the truth. What works for the Bitcoin network also works for any other blockchain network, supply chain included.

*Second, the applications for blockchain in supply chain are far more diverse than making or receiving payments. A large part of this diversity comes from the use of smart contracts.*

A smart contract is a software program that uses blockchain to execute an agreement. The program is stored on the blockchain so the smart contract can only function according to its programming.  No fraud or other interference is possible.

A smart contract can take input from a ledger and trigger an event. For example, after receipt of a payment as part of a transaction, the smart contract can trigger a delivery. Conversely, if a requirement (such as timely delivery or proper storage) is not met as expected the smart contract can trigger a penalty or similar sanction.

Third-party go-betweens are not necessary for the execution of smart contracts. Manual checking of conditions and events are obsolete endeavors. A software program that runs automatically, using information that is guaranteed by the blockchain to be correct, saves both time and money.

##Applications of Blockchain in Supply Chain
The following examples are now in use or can be implemented today using existing technology.

### **Automotive Supplier Payments**
Blockchain allows the transfer of funds anywhere in the world without the need for traditional banking transactions, as transactions are made directly between payer and payee. It is also secure and rapid; taking minutes, compared to days for automated clearing house payments, for example.

Bitcoin transfers specifically also incur lower fees. Australian vehicle manufacturer Tomcar uses Bitcoin to pay some of its suppliers. Currently, three partners in Israel and Taiwan accept payment from Tomcar using Bitcoin.

Tomcar’s supplier agreements use standard terms. The advantage is in the cost savings. On the other hand, the firm is careful to avoid hanging onto too much Bitcoin. While Bitcoin is international by nature, some national governments see it as a way for companies to invest. Companies may therefore be subject to taxation on Bitcoin holdings.

### **Meat Traceability**

Companies can use distributed ledger systems (blockchains) to record product status at each stage of production. The records are permanent and immutable. They make it possible to trace each product to its source. Global retailer Walmart uses blockchain to track sales of pork in China. Its system lets the company see where each piece of meat comes from, each processing and storage step in the supply chain, and the products’ sell-by date. In the event of a product recall, the company can also see which batches are affected and who bought them. 

### **Electric Power Micro-grids**

This example shows how entities of any size can use blockchain. In other words, blockchain is not just for the big players. Smart contracts are being used to redistribute excess power from solar panels. The Transactive Grid is an application running on blockchain to monitor and redistribute energy in a neighbourhood micro-grid. The program automates the buying and selling of green energy to save costs and pollution. The process uses the Ethereum blockchain platform, designed specifically for building and executing smart contracts.

### **RFID-driven Contract Bids and Execution**

RFID tags are commonly used in supply chain to store information about products. IT systems can read the tags automatically and then process them. Therefore, the logic goes; why not use them for smart contracts in logistics?

The possible setup could be as follows. RFID tags for cartons or pallets store information on delivery location and date. Logistics partners run applications to look for these tags and bid for a delivery contract. The partner offering optimal price and service gets the business. A smart contract then tracks status and final delivery performance.

### **Cold Chain Monitoring**

Food and pharmaceutical products often have specialized storage needs. Moreover, enterprises see the value in sharing warehouses and distribution centers instead of each one paying for its own. Sensors on sensitive products can record temperature, humidity, vibration, and other environmental conditions.

These readings can then be stored on a blockchain. They are permanent and tamper-proof. If a storage condition deviates from what is agreed, each member of the blockchain will see it. A smart contract can trigger a response to correct the situation. For instance, depending on the size of the deviation, the action may be to adjust the storage. However, it could also extend to changing “use-by” dates, declaring products unfit, or applying penalties.

### **Blockchain and Internet of Things**

Other ambitious ideas come from using blockchain and the IoT. One suggestion is for smart contracts to manage rentals of driver-less cars. A smart contract could check for rental payments. If there has been no payment or the rental agreement reaches the end of its term, the smart contract could lock the car and tell it to drive itself back to the hire company's premises.

## Real World Supply Chain Blockchains in 2019

It’s always exciting to see how emerging technologies like blockchain progress and develop. Nearly a year after first publishing this article, we are seeing the supply chain and logistics domain evolving into one of the most active sectors for blockchain take-up.

For that reason, it seemed a good idea to document a few examples of how our industry is embracing distributed ledger technology and applying it to solve long-standing business problems. Let’s start with ocean freight shipping, which is an area in which blockchain innovation has been prolific over the last 12 months.

### Tokenizing the Shipping Industry with Ethereum

![shipping-supply-chain](/newImg/blog/blockchain/shipping-supply-chain.jpg)
Hong Kong-based company 300Cubits set out in 2018 to solve an expensive and lingering problem in the container shipping industry—the preponderance of no-shows, when shippers fail to deliver cargo booked on a container vessel, and rolling, which is the outcome of shipping lines’ choice to safeguard vessel utilisation by overbooking to compensate for expected no-shows.

The 300Cubits response to the problem was to issue a TEU cryptocurrency and distribute a quantity of the tokens to shippers and shipping lines in 2018. Participants in the scheme use 300Cubits’ blockchain solution, based on Ethereum, to make deposits using the tokens when cementing a shipping transaction (the contract).

### **How Does the System Work?**

The way it works is that the shipper and the shipping line both deposit a token in the blockchain when the shipper books cargo onto a sailing. To recover their escrowed tokens, both the shipper and the shipping line must honor the booking.

If the shipper does not present the cargo in time for loading on the vessel, the shipping line can recover its token—along with the shipper's token—from escrow. Similarly, if the shipping line fails to transport the shipper's cargo as agreed, the shipper receives both deposited tokens.

While 300Cubits issued the initial batch of tokens, free of charge, to participating shippers and shipping lines, it will sell subsequent batches for payment in fiat currency, ensuring that the tokens acquire monetary value. This value ensures financial consequences for shippers or shipping lines that renege on a booking. A smart contract, executed using the 300Cubit's solution, automatically assigns tokens when a booking is made and reassigns them based on the outcome.

### **How Does Blockchain Solve the Problem?**

The 300Cubits venture essentially makes it less likely that shippers will fail to deliver their cargoes to ports as promised, since it will cost them money to do so. At the same time, the shipping lines will lose out if every time they roll a customer's shipment.

If successful, the scheme should reduce overbooking activity by shipping lines, ensuring that shippers do not suffer delays in the transportation of their cargoes. It should also help shipping lines utilise vessel capacity more effectively.



Blockchain

### Introduction:
Enterprises today face numerous challenges with traditional monolithic application architectures. These challenges include:

**Lack of scalability:** Scaling applications on traditional infrastructure can be a daunting task, especially with the need to manually provision and manage additional resources.

**Difficulty in maintaining uptime:** Applications need to be highly available and resilient to withstand various types of outages, including network failures, hardware failures, and software failures.

**Limited portability**: Applications built on traditional architectures are often tightly coupled to specific hardware and software environments, making it difficult to migrate them to different infrastructures.

**Security vulnerabilities:** Traditional applications are often vulnerable to security threats, including data breaches and unauthorized access, due to lack of proper security measures.

### Cloud Native Architecture:
Cloud Native Architecture is a modern approach to building and deploying applications that addresses the above challenges. Cloud Native applications are designed to run on cloud platforms and take advantage of cloud-native services such as containers, microservices, and serverless computing. The benefits of Cloud Native Architecture include:

##### Scalability: 
Cloud Native applications can be easily scaled up or down using automated tools, making it possible to handle large volumes of traffic without manual intervention.

##### High availability: 
Cloud Native applications are designed to be highly available, with built-in resilience and fault tolerance features that ensure continuous uptime.
Portability: Cloud Native applications are designed to be platform-agnostic, making it possible to deploy them on any cloud platform without significant modifications.

##### Security: 
Cloud Native applications are designed with security in mind, with robust security features built into every layer of the architecture.

#### Azure Kubernetes Service (AKS):
Azure Kubernetes Service (AKS) is a managed Kubernetes service that provides enterprises with a highly available, scalable, and secure platform for deploying Cloud Native applications. AKS provides numerous benefits, including:

**Scalability**: AKS provides automatic scaling of nodes, making it easy to scale applications up or down based on demand.

**High availability:** AKS provides multi-region high availability, ensuring that applications remain available even in the event of a region-wide outage.

**Portability**: AKS provides support for multiple cloud platforms, including Azure, AWS, and Google Cloud, making it easy to migrate applications to different infrastructures.

**Security**: AKS provides industry-standard security measures, including CIS security hardening and support for compliance standards such as PCI DSS.

#### Technovature's AKS Services:
Technovature provides a range of AKS services that help enterprises develop and deploy Cloud Native applications with confidence. These services include:

**AKS setup and configuration:** Technovature helps enterprises set up and configure AKS environments that are optimized for their specific needs.

**AKS migration:** Technovature helps enterprises migrate their existing applications to AKS environments, ensuring that the migration process is smooth and seamless.

**AKS management and support**: Technovature provides ongoing management and support for AKS environments, ensuring that applications remain highly available, scalable, and secure.

#### Conclusion:
Enterprises today need to embrace Cloud Native Architecture to remain competitive and meet the demands of their customers. Azure Kubernetes Service is a powerful platform that provides enterprises with the tools they need to develop and deploy Cloud Native applications with confidence. Technovature's AKS services complement this platform, providing enterprises with the expertise and support they need to succeed in this new era of cloud computing.

Azure Cloud Native Architecture

# Leveraging Cloud-Native and Deep Learning Technologies for Inventory Management

Retail businesses are constantly seeking ways to optimize their operations and enhance customer experience. One area where technology can play a significant role is inventory management. With the advent of cloud-native and deep learning technologies, retailers can now automate their inventory management processes to improve accuracy, reduce costs, and enhance customer satisfaction.

At Technovature, we specialize in building cutting-edge solutions that leverage cloud-native and deep learning technologies. In this article, we will discuss a retail use case that highlights how our expertise in these areas can help businesses achieve their inventory management goals.

#### The Challenge: Manual Inventory Management

Manual inventory management processes can be time-consuming, error-prone, and costly for retailers. Inaccurate inventory counts can lead to overstocking, understocking, and ultimately, lost sales. Additionally, manual inventory management can be challenging during peak shopping periods, such as holidays or sales events, leading to long wait times and frustrated customers.

#### The Solution: Cloud-Native and Deep Learning Technologies

By leveraging cloud-native and deep learning technologies, retailers can automate their inventory management processes to improve accuracy, reduce costs, and enhance customer satisfaction. Specifically, our solution includes the following components:

**Data Collection**: We use sensors and cameras to collect real-time data on inventory levels and customer traffic.

**Data Processing: **We use Apache Kafka to stream the data to Spark, which uses data frame technology to perform transformations and predictions.

**Deep Learning Models:** We use pre-trained deep learning models from NVIDIA to accurately detect and count inventory items.

**Cassandra Database: **We store the processed data in a Cassandra database, which can be easily queried to provide real-time insights on inventory levels and customer behavior.

**Kubernetes Orchestration:** We deploy the solution on a Kubernetes cluster with GPU support to ensure maximum performance and scalability.

#### The Results: Improved Inventory Management and Customer Satisfaction

By leveraging our solution, retailers can achieve the following benefits:

**Accurate Inventory Counts:** Our solution uses deep learning models to accurately detect and count inventory items, leading to more precise inventory management.

**Real-Time Insights: **Our solution provides real-time insights on inventory levels and customer behavior, enabling retailers to make informed decisions and optimize their operations.

**Enhanced Customer Satisfaction:** Our solution reduces wait times and improves the overall shopping experience for customers.

At Technovature, we are committed to helping businesses achieve their technology goals by leveraging cutting-edge solutions such as cloud-native and deep learning technologies. Contact us to learn more about how we can help your business improve its inventory management processes and enhance customer satisfaction.

Deep Learning on Cloud

**A headless CMS is a back-end only content management system (CMS) built from the ground up as a content repository that makes content accessible via a RESTful API for display on any device.**

![headless-cms-freestyle](/newImg/blog/headless-cms/headless-cms-freestyle.png)

## 7 Reasons to Consider a Headless CMS

There are many reasons for a business or an organization to consider a headless content management system (Headless CMS). Among them are a need to use content in multiple channels, a desire for improved customization, and relief from a cluttered, conventional platform.

How a business manages and stores content for products and articles is important. It impacts website and application performance as well as conversions. Thus, some businesses may want to look beyond a traditional CMS.

### Headless CMS

A headless CMS separates content production, management, and storage from distribution and display. This decoupling emphasizes creation, audience building, and business success.

Traditional Content Management Systems (CMS) such as Wordpress have taken a tight-coupled approach towards content storage and content delivery with the proliferation of several plug-ins and extensions that made the whole CMS scene very clumsy, very bulky and a real scary to get into it for a simple use case for content creation and content management.  So many moving parts made it even more difficult to maintain as it was not as easy maintaining code or documents in a Change Management System.

Typical use cases of Traditional Content Management Systems (CMS) has been content creation, content preview and content publishing, all steps linked within a single work flow that led to a tight coupling of the content creation with the content delivery (view or the presentation layer). 

That led to a tight coupling between the content creation and content publishing (viewing) and discouraged reuse of that content sitting in a database and thus restricting other use cases of content consumption.

With the advent of omni-channel user engagement and proliferation of Social Media as a primary mode of Digital Engagement with consumers, it has become essential for organizations to feed content (data) into all these channels. This is where the Headless CMS comes in as a perfect solution 

1. Headless CMS serves as a **central repository** for all the content needs of an organization such as Blogs, Social Media Posts, Application Content all API-driven from the cloud.

2. Headless CMS is designed to encourage content creation that is device for factor or UI **agnostic** but still allows for consumption using **only API** across various devices and channels that have their own view markup and render methods. 

![headlesscms2](/newImg/blog/headless-cms/headlesscms2.jpg)

3.**Non Technical** Users with basic Internet savvy and  Web Application skills can create, modify and deliver content of their choice without having to worry about how it is displayed.

4.**Content Reuse:** Content can now be heavily reused across a multitude of device form factors such as Mobile Smart-phones, Wearables including Smart Watches, Smart Displays and many more such device form-factors.

5.**Content-Mix**: Content stored in a central repository can be **injected** and mixed with secondary forms of content being created by an organization such as Blogs, Social Media Posts, White Papers and Web Pages etc.

6.**Integration:** With the content now available in a free and Open API format such as JSON/REST API, it is much more easier to consume this content via an API Endpoint and integrate it across a wide variety of Enterprise Applications both vertical as well as horizontal. The same API Endpoint can be used even in Customer facing B2C Applications such as E-Commerce Stores, via both Web and Mobile Apps.

7.**Performance:** With content taken off from the same block as the Application stack, the performance of the Web and Mobile Applications can be improved significantly by using latest Front-End Technologies such as ReactJS, ReactNative, GatsbyJS and AngularJS etc.


Headless CMS

# How SPIFFE/Spire is used in a Microservices Environment

SPIFFE (Secure Production Identity Framework For Everyone) and Spire (SPIFFE Runtime Environment) are two open-source projects designed to provide secure, verifiable identities for distributed systems. In a microservices environment, SPIFFE/Spire can be used for authentication and authorization between services, ensuring that only authorized services can communicate with each other.

#### Use Case: E-commerce Microservices

Consider an e-commerce platform consisting of multiple microservices, including an order management service, a user service, and a payment service. Each microservice needs to authenticate and authorize requests from other microservices to ensure that only authorized services can access sensitive data or perform sensitive actions.

By using SPIFFE/Spire, the platform can provide secure identities for each microservice, making it easy to authenticate and authorize requests between services. Here's how it works:

- Each microservice is assigned a unique SPIFFE identity, consisting of a SPIFFE ID and a private key.
- When a microservice needs to make a request to another microservice, it presents its SPIFFE identity to the Spire agent running on the local node.
- The Spire agent validates the SPIFFE identity, ensuring that it is authorized to make requests to the target microservice.
- If the SPIFFE identity is valid, the Spire agent issues a short-lived X.509 certificate to the requesting microservice, signed by the Spire server's root certificate.
- The requesting microservice uses the X.509 certificate to authenticate itself to the target microservice, which in turn verifies the certificate and authorizes the request.
By using SPIFFE/Spire, the e-commerce platform can ensure that only authorized microservices can communicate with each other, preventing unauthorized access to sensitive data or actions.
 

#### Benefits of Using SPIFFE/Spire in a Microservices Environment

Implementing SPIFFE/Spire in a microservices environment provides several benefits, including:

**Stronger Security**
SPIFFE/Spire provides strong security for microservices by using a trust model based on verifiable identities. This ensures that only authorized services can communicate with each other, preventing attacks such as spoofing, man-in-the-middle, and replay attacks.

**Simplified Authentication and Authorization**
SPIFFE/Spire simplifies the authentication and authorization process between microservices by providing secure identities that can be easily validated and verified. This makes it easier to implement access control policies and reduce the risk of errors or misconfigurations.

**Scalability**
SPIFFE/Spire is designed to scale to large, complex microservices environments, making it suitable for use in enterprise-level applications. It can be easily integrated with other tools and services, such as Kubernetes, Istio, and more.

Overall, implementing SPIFFE/Spire in a microservices environment provides a strong security foundation and simplifies the authentication and authorization process between services, making it easier to manage and secure large-scale distributed systems.

### How Technovature Can Help
Technovature is a leading provider of cloud-native solutions and services. We have a team of experts who specialize in designing, implementing, and managing cloud-native solutions for various industries. Our team has extensive experience in leveraging SPIFFE/Spire for microservice authentication and authorization, as well as other cloud-native technologies such as Kubernetes, Istio, and more.

We can help your organization implement SPIFFE/Spire-based authentication and authorization for your microservices in a secure and scalable way. Our team can provide a range of services, including:

- Architecture design and implementation
- SPIFF/Spire integration with your existing microservice infrastructure
- Security and compliance assessments
- Ongoing management and support
We also offer training and workshops to help your team get up to speed with the latest cloud-native technologies and best practices.

At Technovature, we are committed to delivering solutions that meet our clients' unique needs and exceed their expectations. Contact us today to learn more about how we can help you leverage SPIFFE/Spire and other cloud-native technologies to achieve your business goals.

Cloud Native Security with SPIFFE/Spire

DevOps is an essential part of Cloud Native architecture, and it is essential to build a culture of cloud innovation to continuously improve the cloud infrastructure. With the increasing complexity of Cloud Native environments, it is crucial to have an efficient DevOps strategy to ensure continuous delivery. In this article, we will discuss the best practices for Cloud DevOps and how to build a culture of cloud innovation.

### Introduction

Cloud DevOps is the practice of automating the software delivery process in a Cloud Native environment. It is essential to ensure that applications are deployed quickly and efficiently. The goal of Cloud DevOps is to deliver software with high velocity while maintaining quality and stability.

### Cloud Devops Strategy

A good Cloud DevOps strategy has several goals, which include:

**Faster Time-to-Market:** The primary goal of a Cloud DevOps strategy is to enable faster time-to-market for software applications. This is achieved by automating the software delivery process and reducing manual intervention wherever possible.

**Improved Quality:** A good Cloud DevOps strategy ensures that software applications are tested thoroughly at every stage of the software delivery pipeline, which helps to identify and fix issues early on in the development process. This results in improved quality of software applications and reduces the likelihood of defects being found in production.

**Greater Agility: **A Cloud DevOps strategy enables greater agility by allowing software applications to be developed, tested, and deployed quickly and efficiently. This enables teams to respond rapidly to changing market conditions and customer requirements.

**Increased Collaboration:** A good Cloud DevOps strategy fosters collaboration between development, operations, and other teams involved in the software delivery process. This collaboration helps to ensure that everyone is working towards the same goals, and that issues are resolved quickly and efficiently.

**Cost Reduction:** A Cloud DevOps strategy can help to reduce costs by automating many of the manual tasks involved in software delivery. This reduces the amount of time and effort required to deploy software applications, which can result in significant cost savings over time.
The benefits of a good Cloud DevOps strategy are numerous, and include:

**Faster Time-to-Market**: A good Cloud DevOps strategy enables organizations to bring new software applications and features to market faster, which can help them gain a competitive advantage.

**Improved Quality**: A Cloud DevOps strategy ensures that software applications are thoroughly tested at every stage of the software delivery pipeline, which helps to identify and fix issues early on in the development process. This results in improved quality of software applications and reduces the likelihood of defects being found in production.

**Greater Agility: **A Cloud DevOps strategy enables organizations to respond quickly and efficiently to changing market conditions and customer requirements. This agility can help organizations stay ahead of the competition and better serve their customers.

**Increased Collaboration:** A good Cloud DevOps strategy fosters collaboration between development, operations, and other teams involved in the software delivery process. This collaboration helps to ensure that everyone is working towards the same goals, and that issues are resolved quickly and efficiently.

**Cost Reduction**: A Cloud DevOps strategy can help to reduce costs by automating many of the manual tasks involved in software delivery. This reduces the amount of time and effort required to deploy software applications, which can result in significant cost savings over time.

Overall, a good Cloud DevOps strategy can help organizations improve their software delivery process, reduce costs, and gain a competitive advantage in the marketplace.

### Building a culture of cloud innovation

To build a culture of cloud innovation, organizations must focus on the following:

**Collaboration**: Collaboration between teams is crucial for a successful Cloud DevOps strategy. DevOps is a cross-functional approach that requires collaboration between development and operations teams.

**Innovation**: Innovation is essential to stay ahead of the competition. Teams must be encouraged to think outside the box and come up with innovative solutions to improve the cloud infrastructure.

**Automation**: Automation is a key factor in Cloud DevOps. It allows teams to focus on innovation and reduces the time and effort required for manual tasks.
Continuous Learning: Continuous learning is crucial in a rapidly evolving Cloud Native environment. Teams must be encouraged to keep learning new technologies and best practices to stay up-to-date with the latest trends and developments.

### Cloud DevOps Best Practices

Here are some best practices for Cloud DevOps:

**Infrastructure as Code (IaC):** IaC is a practice of automating infrastructure management using code. It helps to eliminate manual configuration errors and improves consistency across environments.

**Continuous Integration (CI)**: CI is the practice of automatically building, testing, and validating changes to the codebase. It helps to identify issues early in the development cycle and ensures that the code is of high quality.

**Continuous Delivery (CD):** CD is the practice of automatically deploying code changes to production. It ensures that the code is released quickly and efficiently.

**Monitoring and Observability**: Monitoring and observability are crucial in Cloud DevOps. It helps to identify issues and prevent outages.
Security: Security is a key consideration in Cloud DevOps. Teams must ensure that security is built into every stage of the development cycle.

### Cloud Finops strategy for optimizing cloud computing costs

Cloud Finops is the practice of optimizing cloud computing costs. It is essential to ensure that organizations are getting the best value for their money. Here are some best practices for Cloud Finops:

**Cost monitoring**: Organizations must monitor their cloud costs regularly. It helps to identify areas where costs can be optimized.
Reserved Instances: Reserved Instances are a great way to save money on cloud computing costs. Organizations can save up to 70% by reserving instances for a longer duration.

**Right-sizing**: Right-sizing is the practice of matching the resources to the workload. It helps to optimize cloud computing costs and improve performance.
Automation: Automation is a key factor in Cloud Finops. It allows teams to optimize costs without sacrificing performance.

**Cost Allocation:** Cost allocation is the practice of assigning costs to different departments or teams. It helps to identify areas where costs can be optimized.

### Conclusion

In conclusion, building a culture of cloud innovation is crucial in a Cloud Native environment. Teams must focus on collaboration, innovation, automation, and continuous learning. Cloud DevOps best practices such as IaC, CI, CD, monitoring, observability, and security are essential to ensure continuous delivery. Cloud Finops best practices such as cost monitoring, reserved instances, right-sizing, automation, and

## What is a Full Stack developer?

A full-stack web developer is a technology expert who can work on both in the front end as well the back-end of any application. The person should be familiar with each layer of a 3-tier model. The 3-tier consists of

- The presentation layer ( Main front end portion which deals with the user interface),

- Business Logic Layer (back-end portion of any application which deals with data validation)

- Database Layer

A Full-Stack Developer doesn't necessarily master of all technologies. However, the professional is expected to work on the client as well as server sides and understand what is going on when developing an application. He or she should have a genuine interest in all software technologies.

![mern](/newImg/blog/fullstack/mern.jpg)

Full Stack Development

## How can Big Data be used to improve decision-making?
 

Big data can be used to improve decision-making by allowing businesses to analyse customer data to identify trends and patterns. This can help businesses to target their customers more effectively and improve their products and services.

### 1. Identifying trends and patterns

Businesses can use big data to identify trends and patterns in customer data. This can help them to understand what products and services their customers want and need.

Big data is a huge buzzword these days, and for good reason. The ability to collect and analyze large amounts of data can provide tremendous insights into what's happening in the world. And for businesses, this can mean the ability to make better decisions and optimize operations in order to stay ahead of the competition. 

### 2. Targeting customers

Big data can be used to target customers in a number of ways. First, by analyzing data about customer behavior, companies can identify patterns that can be used to predict what customers might want or need. This information can then be used to create targeted advertisements and promotions.

Second, companies can use big data to understand how customers interact with their products and services. This information can help companies improve customer service and make changes to their products and services to better meet customer needs.

Finally, by analyzing customer data, companies can identify which customers are most profitable and target them with specific marketing campaigns and others.


 
## Using Big Data Decision Making to Target Customers - A Case Study
 

Big data can be used to target customers in a number of ways. First, by analyzing data about customer behavior, companies can identify patterns that can be used to predict what customers might want or need. This information can then be used to create targeted advertisements and promotions.

Second, companies can use big data to understand how customers interact with their products and services. This information can help companies improve customer service and make changes to their products and services to better meet customer needs.

Finally, by analyzing customer data, companies can identify which customers are most profitable and target them with products and services.

Big Data in Decision making

# The Power of Generative AI: Revolutionizing Business and Enterprise

### Introduction
Generative AI, a subset of artificial intelligence, has the potential to revolutionize the way businesses operate. Unlike traditional AI, which is designed to solve specific problems or perform pre-defined tasks, generative AI can create entirely new content or data based on patterns and rules it learns from existing data. This ability to generate new content or data can be particularly valuable for businesses looking to create personalized content, streamline processes, and gain insights into complex data sets.

### Practical Applications

##### Marketing & Advertising

One area where generative AI is already making an impact is in marketing and advertising. By analyzing vast amounts of data on consumer behavior, preferences, and habits, generative AI can create personalized marketing messages and ads that are tailored to each individual customer. This can help businesses increase engagement and conversions while reducing ad spend.

##### Product Design & Development
Another area where generative AI is being used is in product design and development. By analyzing user data and feedback, generative AI can create new designs or product concepts that are more aligned with customer needs and preferences. This can lead to faster product development cycles and a more iterative approach to design and development.

##### Finance & Healthcare
Generative AI is also being used in industries such as finance and healthcare to analyze and interpret complex data sets. By training AI models to identify patterns and trends in large datasets, businesses can gain insights that were previously hidden or difficult to uncover. For example, in finance, generative AI can be used to analyze market data and identify trends that could impact investments or trading strategies. In healthcare, generative AI can be used to analyze patient data and identify potential risks or treatment options.

One key advantage of generative AI is that it can be used to automate tasks that are typically done by humans. This can free up valuable time and resources, allowing businesses to focus on more strategic initiatives. For example, in customer service, generative AI can be used to create chatbots that can interact with customers in a natural language and answer common questions. This can help businesses reduce wait times and improve customer satisfaction.

### Risks

However, there are also potential risks associated with generative AI. For example, there is a risk that AI models could inadvertently learn biases from the data they are trained on. Additionally, there is a risk that generative AI could be used to create fake or misleading content, such as deepfake videos or fake news articles.

To mitigate these risks, businesses must be careful to ensure that their AI models are trained on diverse and representative data sets. They must also be transparent about how their AI systems are being used and take steps to address any unintended consequences that may arise.

### Conclusion

In conclusion, generative AI has the potential to transform the way businesses operate, from marketing and advertising to product design and development. However, as with any technology, there are risks associated with its use. Businesses must take a responsible approach to the development and deployment of generative AI, ensuring that it is used ethically and in a way that benefits both the business and its customers.

## How can we help
Technovature can help customers harness the power of generative AI to drive business growth and innovation. Here are a few ways Technovature can help:

### Assessing business needs
 Technovature can work with customers to identify areas where generative AI can be used to improve business operations, whether it's in marketing and advertising, product design and development, or other areas of the business.

### Developing AI models
Technovature has the expertise to develop custom AI models that are tailored to the specific needs of each customer. This can include training AI models to analyze complex data sets, generate personalized content, or automate tasks that are typically done by humans.

### Integration with existing systems
 Technovature can help customers integrate generative AI systems with their existing business processes and systems. This can help ensure that the AI systems are seamlessly integrated into the business and are working efficiently and effectively.

### Monitoring and maintenance
 Technovature can provide ongoing monitoring and maintenance of generative AI systems to ensure that they are working as intended and are delivering the expected results. This can help identify and address any issues that arise, and ensure that the AI systems continue to deliver value over time.

### Ethical considerations
 Technovature is committed to helping customers use generative AI in an ethical and responsible manner. They can provide guidance on best practices for training AI models, avoiding bias, and ensuring that the AI systems are being used in a way that benefits both the business and its customers.

Overall, Technovature can provide customers with the expertise and guidance they need to leverage generative AI to drive innovation and growth in their businesses. We can help businesses identify new opportunities for AI, develop custom AI solutions, integrate them with existing systems, and ensure that they are being used in an ethical and responsible way.

Generative AI for Business

### Introduction:

As organizations move their applications to cloud-native architectures, they need to rethink their security approach to protect their assets from new and evolving threats. Traditional security models based on perimeter defenses are no longer sufficient in the dynamic and distributed nature of cloud-native environments. This is where the concept of Zero Trust architecture comes into play.

Zero Trust architecture is a security model that assumes that any user or system, whether inside or outside the network perimeter, should not be automatically trusted. Instead, it enforces strict access controls and authentication policies to verify the identity and authorization of every request before granting access to resources.

In this article, we will explore the key principles and benefits of Zero Trust architecture in the context of cloud-native security, Kubernetes, Service Mesh technology, and microservices architecture. We will also discuss the challenges and complexities involved in implementing Zero Trust architecture and how Technovature, with its expertise in cloud-native technologies, can help organizations adopt this security model effectively. Finally, we will examine the role of SPIFFE and SPIRE technologies in enabling Zero Trust security for cloud-native environments.

#### Zero Trust Architecture and Cloud Native Security

Zero Trust Architecture (ZTA) is an approach to security that assumes that all network traffic is untrusted, and requires strict access controls, authentication, and authorization for all users, devices, and services. This is especially important in the context of cloud native environments, where applications and services are distributed across multiple containers and pods running on different nodes in a Kubernetes cluster.

To implement ZTA in a cloud native environment, it is important to have a strong authentication and authorization mechanism, as well as a secure communication channel between services. This is where SPIFFE and SPIRE technologies come in.

#### SPIFFE and SPIRE
SPIFFE (Secure Production Identity Framework For Everyone) is an open-source project that provides a standardized way to authenticate and authorize services in a cloud native environment. SPIFFE defines a set of protocols and APIs that allow services to prove their identity to other services, without relying on traditional network-based authentication methods.

SPIRE (SPIFFE Runtime Environment) is an implementation of SPIFFE that provides a distributed and scalable framework for managing and distributing identity to services in a cloud native environment. SPIRE uses X.509 certificates to securely identify services, and provides a secure communication channel between services using mutually authenticated TLS connections.

#### Service Mesh Technology
Service mesh technology is another important component of a cloud native security architecture. A service mesh is a dedicated infrastructure layer for managing service-to-service communication within a Kubernetes cluster. It provides a set of tools and services that enable secure, reliable, and observable communication between services.

One of the key benefits of a service mesh is the ability to enforce security policies at the network level, without requiring changes to the application code. This allows security policies to be centrally managed and enforced across all services in the cluster.

#### Microservices Architecture
Finally, a microservices architecture is a key component of a cloud native security architecture. Microservices architecture allows for the separation of concerns, which means that each microservice can have its own security policies and access controls. This allows for more granular security policies, and makes it easier to manage and scale individual microservices.

### Technovature's Expertise and Value Proposition

At Technovature, we have extensive experience in building and implementing cloud native security architectures using Kubernetes, service mesh technology, and microservices architecture. Our team of experts can help you design and implement a Zero Trust Architecture for your cloud native environment, using the latest security technologies and best practices.

We have a deep understanding of SPIFFE and SPIRE technologies, and can help you integrate these technologies into your cloud native environment to provide a secure and reliable communication channel between services.

Our team also has extensive experience in implementing service mesh technology, and can help you design and deploy a service mesh that meets your specific security requirements.

Finally, our expertise in microservices architecture means that we can help you design and implement a security architecture that is tailored to the needs of your individual microservices.

At Technovature, we are committed to providing our clients with the latest cloud native security technologies and best practices, and to helping them build secure, reliable, and scalable cloud native applications.

#### Conclusion

In conclusion, adopting a zero trust architecture is crucial for modern organizations to protect their cloud infrastructure against cyber threats. A zero trust approach can enable organizations to enforce fine-grained access control policies, implement strong authentication and authorization mechanisms, and detect and respond to security incidents in real-time.

However, implementing a zero trust architecture requires expertise in several areas such as cloud native security, Kubernetes, service mesh technology, and microservices architecture. Technovature has the technical expertise and experience to help organizations design and implement a zero trust architecture that meets their specific security needs.

Additionally, by leveraging SPIFFE and SPIRE related technologies, organizations can enhance their zero trust architecture to provide secure communication between microservices within a Kubernetes cluster or across multiple clusters.

At Technovature, we are committed to helping organizations adopt a zero trust architecture that ensures their cloud infrastructure remains secure and resilient against cyber threats. Contact us today to learn more about how we can help your organization achieve its security objectives.



Zero Trust Architecture

# Cloud Native Observability: A Comprehensive Stack for Kubernetes Monitoring and Alerting

Cloud-native technologies have revolutionized the way modern applications are built, deployed, and managed. Kubernetes, the leading container orchestration platform, has become the de-facto standard for deploying cloud-native applications due to its flexibility, scalability, and portability. However, managing Kubernetes clusters and the applications running on them can be challenging, especially when it comes to monitoring and alerting.

**Observability** is the practice of gaining insights into the performance and behavior of complex distributed systems. It involves collecting, processing, and analyzing various data points, including metrics, logs, and traces, to understand how a system is behaving and identify potential issues before they become critical. Observability is critical for cloud-native environments because of the distributed and dynamic nature of these systems.

To achieve effective observability in a Kubernetes environment, you need a comprehensive stack that can collect, store, and visualize various data points from your infrastructure and applications. In this article, we'll explore a cloud-native observability stack that combines the strengths of Prometheus, Grafana, Elasticsearch, Jaeger Tracing, and Kibana.

### Prometheus: A Powerful Metrics Collection and Alerting System

Prometheus is a popular open-source monitoring system that collects metrics from various sources, including Kubernetes, and stores them in a time-series database. Prometheus provides a powerful query language that allows you to analyze and aggregate metrics and create custom alerts based on specific conditions. Prometheus has become the de-facto standard for collecting metrics in Kubernetes environments due to its flexibility, scalability, and ease of use.

### Grafana: A Flexible and Customizable Dashboarding Tool

Grafana is an open-source data visualization tool that allows you to create custom dashboards and panels to visualize your data. Grafana integrates with various data sources, including Prometheus, and provides a rich set of visualizations and plugins to customize your dashboards. Grafana also supports alerting, allowing you to create custom alert rules based on specific conditions.

### Elasticsearch: A Distributed Search and Analytics Engine

Elasticsearch is a powerful open-source search and analytics engine that allows you to store, search, and analyze large volumes of data in real-time. Elasticsearch is particularly useful for storing and searching logs and provides powerful search and aggregation capabilities. Elasticsearch integrates with Kibana, a data visualization tool, to provide a complete solution for log management and analysis.

### Jaeger Tracing: A Distributed Tracing System

Jaeger is an open-source distributed tracing system that allows you to trace requests through complex distributed systems. Jaeger provides a complete end-to-end view of requests, including timing data, service dependencies, and error rates. Jaeger integrates with various frameworks and libraries, including Kubernetes, to provide a comprehensive tracing solution for cloud-native environments.

### Kibana: A Data Visualization Tool for Elasticsearch

Kibana is an open-source data visualization tool that allows you to create custom dashboards and visualizations based on data stored in Elasticsearch. Kibana provides a rich set of visualization options and integrates with various data sources, including Elasticsearch, to provide a complete solution for log management and analysis.

### How Technovature Can Help You Achieve Effective Cloud Native Observability
 Technovature, being a Cloud Native Software Consultancy company, can provide end-to-end solutions for clients to achieve effective Cloud Native observability for their platform and applications. Our team of experts has extensive experience in building and deploying Cloud Native applications and can help clients implement an observability stack tailored to their specific needs.

Our approach involves working closely with clients to understand their business objectives, identifying key performance indicators (KPIs), and designing an observability stack that can provide real-time insights and actionable data to help clients make informed decisions. We can help clients implement the latest open-source technologies such as Prometheus, Grafana, Elasticsearch, Jaeger Tracing, and Kibana, and integrate them into their existing infrastructure.

Furthermore, our team can help clients with setting up automated alerts and notifications based on predefined thresholds and integrating observability with other tools such as incident management and DevOps workflows. With Technovature's expertise in Cloud Native observability, clients can gain valuable insights into their applications and infrastructure, leading to improved performance, faster incident resolution, and better business outcomes.

Cloud Native Observability

# Building a World-Class Cloud Native Infrastructure Powered by Azure DevOps

In today's fast-paced world, businesses need to keep up with the latest technologies to remain competitive. The adoption of cloud-native infrastructure has become crucial to building scalable and agile systems that can handle the demands of modern applications. Microsoft Azure, one of the leading cloud providers, offers Azure DevOps, a powerful set of tools that can help organizations build a world-class cloud-native infrastructure powered by Kubernetes and other advanced technologies.

### Azure DevOps: An Introduction

Azure DevOps is a cloud-based service offered by Microsoft that provides an integrated set of features that enable organizations to plan, build, test, and deploy applications. It offers a comprehensive set of tools that help teams manage their workflows and collaboration efforts more efficiently. Azure DevOps is composed of five main services: Boards, Pipelines, Repos, Artifacts, and Test Plans.

#### Azure DevOps and Cloud Native Infrastructure

Cloud-native infrastructure is a modern approach to building and running applications that takes full advantage of the cloud. It's an architecture that is optimized for scalability, reliability, and performance, and it leverages a set of technologies, such as Kubernetes, to achieve these goals. Azure DevOps can help organizations build a cloud-native infrastructure by providing the necessary tools to manage the entire DevOps pipeline.

#### Azure DevOps and Kubernetes

Kubernetes is a powerful open-source platform for managing containerized workloads and services. It's a core technology for building cloud-native infrastructure, and it's widely adopted in the industry. Azure DevOps offers seamless integration with Kubernetes, making it easy to deploy and manage applications on the platform. With Azure DevOps, teams can build, test, and deploy their Kubernetes applications with ease, and they can take advantage of features such as automated rollouts and rollbacks, automatic scaling, and more.

### Azure DevOps and Technovature

At Technovature, we understand the importance of cloud-native infrastructure and the role that Azure DevOps and Kubernetes play in achieving this goal. We offer a range of services that can help organizations build a world-class cloud-native infrastructure powered by these advanced technologies. Our team of experts has extensive experience in using Azure DevOps and Kubernetes to build scalable, reliable, and high-performance systems that can handle the demands of modern applications. We are fully tuned into the Cloud Native Computing Foundation and actively follow and contribute to various projects within the CNCF. Our value proposition is to help organizations achieve their cloud-native infrastructure goals by leveraging our technical expertise and our commitment to delivering innovative solutions.

Use Case: Building a Cloud Native Infrastructure for a Retail Giant

A major retail company with a presence in multiple countries needed to modernize its IT infrastructure to keep up with the demands of its customers. The company wanted to build a cloud-native infrastructure that could handle the scale and complexity of its operations. Technovature worked with the company to design and implement a solution based on Azure DevOps and Kubernetes.

The solution involved migrating the company's existing applications to containers and deploying them on Kubernetes clusters. Technovature used Azure DevOps to automate the entire DevOps pipeline, from building and testing to deployment and monitoring. The solution also included advanced features such as automated scaling, self-healing, and auto-recovery.

The result was a highly scalable and reliable cloud-native infrastructure that could handle the company's growing demands. The solution also helped the company reduce its IT costs by optimizing resource utilization and improving application performance.

#### Conclusion

In conclusion, Azure DevOps provides a comprehensive and powerful suite of tools for managing and deploying cloud-native applications powered by Kubernetes. With its seamless integration with Azure Cloud services and its support for open-source technologies, Azure DevOps enables organizations to build and manage a world-class cloud-native infrastructure that is both efficient and secure.

At Technovature, we are committed to helping our clients adopt and implement the latest technologies and best practices in cloud-native computing, including Azure DevOps and Kubernetes. Our team of experts has extensive experience in designing, building, and managing cloud-native applications and infrastructure, and we are fully equipped to help organizations achieve their cloud-native goals. Contact us today to learn more about how Technovature can help your organization leverage the power of Azure DevOps and Kubernetes for building a scalable and secure cloud-native infrastructure.





Top Azure Cloud Developer in Hyderabad | Technovature

## Introduction:
Software as a Service (SaaS) has become a popular delivery model for software applications, allowing businesses to reduce their costs and improve their scalability. Multi-tenancy is a key feature of SaaS, enabling multiple customers to share a single instance of the software application. Cloud-native computing principles can help organizations design and deploy a robust, scalable, and secure multi-tenant SaaS architecture. In this article, we'll explore the key considerations and best practices for building a multi-tenant SaaS cloud architecture using cloud-native computing principles.

### Multi-tenant SaaS Architecture:
A multi-tenant SaaS architecture consists of a single software instance that serves multiple customers or tenants. Each tenant has their own set of data and configurations, but they share the same application codebase, infrastructure, and resources. A well-designed multi-tenant SaaS architecture can provide scalability, efficiency, and security benefits. Here are some key considerations for building a multi-tenant SaaS architecture:

**Tenant Isolation:** Tenants should be isolated from each other to ensure data privacy and security. This can be achieved through logical or physical isolation of data and infrastructure.

**Scalability**: The architecture should be designed to scale horizontally to meet the growing demands of tenants. This can be achieved through the use of containerization, orchestration, and auto-scaling technologies.

**Resilience**: The architecture should be fault-tolerant and resilient to ensure high availability and minimal downtime for tenants. This can be achieved through the use of redundant and distributed infrastructure and application components.

**Monitoring and Logging:** The architecture should provide robust monitoring and logging capabilities to ensure timely identification and resolution of issues.

**Security**: The architecture should be designed with security in mind, including data encryption, access controls, and authentication mechanisms.

##### Use Case:
Let's consider a use case in the healthcare industry. A healthcare provider wants to build a multi-tenant SaaS platform for managing patient data and electronic health records (EHRs) for multiple hospitals and clinics. The platform should be scalable, secure, and compliant with regulatory requirements such as HIPAA. The platform should also be customizable to meet the unique needs of each hospital or clinic.

To achieve these requirements, the multi-tenant SaaS architecture can be designed using cloud-native computing principles. The platform can be built using microservices architecture and deployed on an Azure Kubernetes Service (AKS) cluster. Each microservice can be containerized using Docker and managed using Kubernetes. The platform can also utilize cloud-native technologies such as KEDA for auto-scaling and Istio for service mesh.

#### Technovature's Offering:
At Technovature, we provide end-to-end cloud-native computing solutions for building scalable and secure multi-tenant SaaS architectures. Our services include cloud-native architecture design, microservices development, containerization, and orchestration using Kubernetes and other cloud-native technologies. We also provide managed services for monitoring, logging, and security of your multi-tenant SaaS platform. With our expertise in cloud-native computing, we can help you build a robust and scalable multi-tenant SaaS platform that meets your business needs and regulatory requirements.

#### Conclusion:
Multi-tenant SaaS architecture using cloud-native computing principles can provide scalability, efficiency, and security benefits for businesses. The architecture should be designed with tenant isolation, scalability, resilience, monitoring and logging, and security considerations in mind. With cloud-native technologies such as Kubernetes, KEDA, and Istio, building a multi-tenant SaaS platform can be achieved easily. Technovature can help businesses design and deploy a cloud-native multi-tenant SaaS architecture that meets their unique business requirements and regulatory compliance needs.

Building Multi Tenant SaaS Solutions 

Kubernetes is a key technology in the cloud-native ecosystem.

## What is Kubernetes?

Kubernetes is an open-source container orchestration platform that automates the deployment, scaling, and management of containerized applications. It was originally developed by Google and is now maintained by the Cloud Native Computing Foundation (CNCF). Kubernetes provides a robust set of features that make it easy to manage containers at scale, including automated container deployment, scaling, and orchestration, as well as automated rollouts and rollbacks, self-healing, and automated scaling.

## What does it mean to be cloud-native?

Being cloud-native means developing applications that are optimised for the cloud environment. Cloud-native applications are designed to be scalable, resilient, and flexible, and they take advantage of cloud technologies such as containers, microservices, and serverless architectures. These applications are designed to run in a cloud environment, whether that's a public cloud, a private cloud, or a hybrid cloud.

To be cloud-native with Kubernetes, developers must take advantage of Kubernetes' features to design and deploy applications that are optimised for the cloud. Kubernetes provides a number of key features that enable cloud-native development, including:

**Containerization**: Kubernetes makes it easy to deploy and manage containerized applications, which are lightweight and portable.

**Scalability**: Kubernetes enables automatic scaling of containers to meet changing demand, ensuring that applications can handle traffic spikes without downtime.

**Resiliency**: Kubernetes provides self-healing capabilities that ensure that applications can recover from failures without manual intervention.

**Flexibility**: Kubernetes enables flexible deployment options, including on-premises, in the cloud, or in a hybrid environment.

**Automation**: Kubernetes automates many of the tasks associated with application deployment, scaling, and management, reducing the burden on developers and IT operations teams.


## Benefits for businesses

Certainly, there are many benefits for businesses in adopting a cloud-native approach with Kubernetes. Here are some of the key advantages:

**Faster Time to Market**: With Kubernetes' automation capabilities and cloud-native architecture, businesses can develop and deploy applications faster, enabling them to bring new products and services to market more quickly.

**Greater Scalability**: Kubernetes enables automatic scaling of containers to meet changing demand, ensuring that businesses can handle traffic spikes without downtime. This can be especially beneficial for businesses with unpredictable or seasonal demand.

**Increased Resilience**: Kubernetes' self-healing capabilities ensure that applications can recover from failures without manual intervention, reducing the risk of downtime and improving application reliability.

**Improved Efficiency**: By automating many of the tasks associated with application deployment, scaling, and management, Kubernetes can free up IT operations teams to focus on other areas of the business.

**Lower Costs**: Kubernetes' containerization and automation capabilities can help businesses reduce infrastructure costs by optimising resource utilisation and minimising downtime.

In summary, adopting a cloud-native approach with Kubernetes can help businesses to innovate faster, scale more efficiently, and reduce costs, ultimately enabling them to compete more effectively in today's fast-paced digital landscape.

## How to build cloud-native applications with Kubernetes?

To build cloud-native applications with Kubernetes, developers should follow a few key principles:

Use **containerization**: Develop applications using containers to ensure that they are lightweight and portable.

Use **microservices**: Break applications down into small, independent services to enable greater flexibility and resilience.

Use **automation**: Use Kubernetes' automation capabilities to automate deployment, scaling, and management tasks.

Use **cloud-native services**: Take advantage of cloud-native services such as serverless computing and managed databases to simplify application development and deployment.

Use **continuous delivery**: Implement continuous delivery practices to enable rapid iteration and deployment of new features and bug fixes.

Kubernetes is a powerful tool for building cloud-native applications. By leveraging Kubernetes' features, developers can design and deploy applications that are scalable, resilient, and flexible. Cloud-native applications are designed to run in a cloud environment, taking advantage of cloud technologies such as containers, microservices, and serverless architectures. 

With Kubernetes, developers can automate many of the tasks associated with application deployment, scaling, and management, enabling faster development cycles and more frequent deployments.


How adopting Kubernetes for business can increase efficiency and save money

### Introduction:

The rapid proliferation of Internet of Things (IoT) devices and their use cases is driving the need for high-bandwidth, low-latency networks. 5G technology promises to deliver on this need with its ability to provide extremely fast data transfer rates and low latency. However, the deployment of 5G networks also requires a distributed infrastructure that can handle the large amounts of data generated by IoT devices at the network edge, and a scalable core network in the cloud that can support the processing of this data.

### Problem Statement:
As cities become more populous, there is an increasing need for smart city solutions that can manage a wide range of services, such as transportation, energy, water, waste management, and public safety. Smart city solutions require a large number of sensors and devices to collect data in real-time, which can then be analyzed to provide actionable insights. However, collecting, storing, and analyzing such a massive amount of data requires a powerful infrastructure that can handle the load, while also being scalable, secure, and reliable. This is where 5G, edge computing, and Kubernetes come into play.

### Solution:

One solution to this challenge is to leverage Kubernetes for both the edge network and the core network in the cloud. Kubernetes is a powerful container orchestration system that simplifies the management and deployment of containerized applications. It provides a scalable, fault-tolerant, and automated infrastructure that can be used for both the edge and core networks.

In this solution, the edge network is composed of a set of microservices deployed as containers on Kubernetes clusters. These microservices provide the required functionality for the edge network, such as data collection from IoT sensors, processing and analysis of this data, and storage of the results. Kubernetes provides the necessary features for managing the edge network, such as service discovery, load balancing, and automatic scaling.

The core network, on the other hand, is responsible for processing and analyzing the large amounts of data generated by the edge network. The core network can be deployed on a cloud-native infrastructure, using Kubernetes as the orchestration system. The core network can include a variety of data processing technologies, such as Apache Kafka, Spark, and Hadoop, as well as machine learning algorithms and other advanced analytics tools.

The core network also includes a data analytics layer that provides real-time visibility and control over the various sensors in a smart city solution. This layer can include dashboards and visualizations that enable users to monitor and manage the data generated by the sensors, and to take appropriate actions based on this data.

### Benefits:

Using Kubernetes for both the edge and core networks in a 5G solution provides several benefits, including:

**Scalability**: Kubernetes provides a scalable infrastructure that can handle large amounts of data generated by IoT devices, enabling the 5G network to support the growth of IoT deployments.
Fault tolerance: Kubernetes provides high availability and fault tolerance, ensuring that the 5G network can continue to function even in the event of failures or downtime.

**Automation**: Kubernetes automates many tasks associated with managing a large-scale distributed infrastructure, reducing the need for manual intervention and improving the efficiency of operations.

**Flexibility**: Kubernetes is a flexible system that can support a variety of workloads and technologies, making it easier to deploy and manage a diverse set of applications and services.

**Real-time visibility and control: **The data analytics layer in the core network provides real-time visibility and control over the various sensors in a smart city solution, enabling users to monitor and manage the data generated by the sensors and take appropriate actions based on this data.

### Conclusion:

In conclusion, Kubernetes is a powerful technology that can be used to build scalable, fault-tolerant, and automated 5G networks that can support a wide range of IoT use cases. By using Kubernetes for both the edge and core networks, organizations can build distributed infrastructures that can handle large amounts of data generated by IoT devices, while also providing real-time visibility and control over the sensors in a smart city solution. This approach provides significant benefits, including scalability, fault tolerance, automation, flexibility, and real-time visibility and control, making it an ideal solution for 5G networks in smart cities and other IoT use cases.

Next Gen Smart Cities 

The Great Gatsby

### Introduction
In today's fast-paced business world, events are happening all around us, and organizations need to be able to capture, process, and respond to these events in real-time to stay ahead of the competition. This is where Cloud Native Event Orchestration comes into play. By leveraging open source and Azure Cloud services, businesses can build event-driven architectures that are scalable, resilient, and secure.

#### What is Cloud Native Event Orchestration?

Cloud Native Event Orchestration is a way to build event-driven architectures that are natively integrated with cloud-native technologies. This approach allows organizations to leverage the scalability, resilience, and security of cloud-native technologies, such as Kubernetes, while also leveraging open-source tools, such as Apache Kafka and Apache Flink, to capture and process events.

#### Challenges of Building Cloud Native Event Orchestration Services

Building a Cloud Native Event Orchestration service can be challenging, particularly for organizations that are new to event-driven architectures. Some of the key challenges include:

**Capturing and processing events in real-time:** Organizations need to be able to capture and process events in real-time to stay ahead of the competition.

**Ensuring scalability: **As the number of events grows, the event orchestration service needs to be able to scale seamlessly.

**Ensuring resilience:** The event orchestration service needs to be able to withstand failures and recover quickly.

**Ensuring security**: The event orchestration service needs to be secure, particularly when dealing with sensitive data.

#### Building a Cloud Native Event Orchestration Service using Open Source and Azure Cloud Services

To build a Cloud Native Event Orchestration service, organizations can leverage open-source tools, such as Apache Kafka and Apache Flink, to capture and process events, while also leveraging Azure Cloud services, such as Azure Kubernetes Service (AKS), Azure Event Grid, and Azure Functions, to provide scalability, resilience, and security.

Here are some of the steps involved in building a Cloud Native Event Orchestration service using open source and Azure Cloud services:

**Choose an event stream processing framework:** Apache Flink is a popular open-source framework for event stream processing that is widely used in the industry. It provides high-throughput, low-latency event processing, fault-tolerance, and state management.

**Choose an event stream messaging system:** Apache Kafka is a popular open-source messaging system for event-driven architectures that is widely used in the industry. It provides a distributed and scalable messaging system that can handle millions of events per second.

**Deploy Apache Flink and Apache Kafka on Azure Kubernetes Service (AKS): **AKS provides a scalable, secure, and managed Kubernetes service that can be used to deploy Apache Flink and Apache Kafka clusters.

**Configure Apache Flink to read events from Apache Kafka: **Apache Flink can be configured to read events from Apache Kafka and process them in real-time.

**Build event-driven Azure Functions:** Azure Functions is a serverless compute service that allows organizations to run event-driven code in the cloud. Organizations can use Azure Functions to build event-driven workflows that respond to events in real-time.

**Configure Azure Event Grid to trigger Azure Functions: **Azure Event Grid is a fully-managed event routing service that allows organizations to build event-driven architectures. Organizations can use Azure Event Grid to route events to Azure Functions based on event subscriptions.

By following these steps, organizations can build a Cloud Native Event Orchestration service that is scalable, resilient, and secure. By leveraging open source and Azure Cloud services, organizations can build event-driven architectures that can capture and process events in real-time, ensuring they stay ahead of the competition.

#### Conclusion
In conclusion, building a Cloud Native Event Orchestration service using open source and Azure Cloud services is a powerful way to streamline and automate event-driven processes within an organization. By leveraging the power of cloud-native architectures and event-driven paradigms, businesses can gain a competitive edge and improve their operational efficiency.

At Technovature, we understand the complexities involved in building and managing event-driven applications. Our team of experts has extensive experience in designing and implementing Cloud Native Event Orchestration services that cater to the unique needs of businesses across various industries.

We can help you leverage the power of open-source technologies such as Apache Kafka and Kubernetes, as well as Azure Cloud services such as Event Grid and Logic Apps, to build an event-driven architecture that can handle millions of events in real-time.

Whether you're looking to modernize your existing event-driven applications or build new ones from scratch, Technovature can provide end-to-end support, from architecture design to deployment and ongoing maintenance. With our expertise in cloud-native technologies and event-driven architectures, we can help you build scalable and reliable applications that can transform the way your business operates.

Cloud Event Orchestration

## Introduction

The supply chain is a complex and vital aspect of many industries, spanning multiple stages from production to delivery. Today, many supply chain processes still rely on paper-based documentation and outdated systems, leading to delays, inefficiencies, and a lack of transparency. With the increasing demand for speed, accuracy, and sustainability, it is clear that the traditional supply chain management approach is no longer sufficient to meet the needs of modern businesses.

### Problem Statement

One of the main challenges facing supply chains today is the lack of digitization. This leads to inefficiencies and delays and increases the risk of errors, fraud, and counterfeiting. Traditional supply chain management systems are often siloed, making it difficult to share information across different stages of the supply chain. As a result, many companies struggle to gain visibility into their supply chain operations and identify potential issues before they become significant problems. This lack of visibility can also impact consumer trust and confidence in the supply chain, which can have far-reaching consequences for businesses.

### Solution
Blockchain technology offers a promising solution to many of the challenges facing supply chains today. By leveraging blockchain, IoT, edge networks, tokens, smart contracts, and cloud-based digital supply chain solutions, companies can create a secure, transparent, and efficient supply chain ecosystem.

The use of blockchain technology enables supply chain participants to securely and transparently track goods and transactions at every stage of the supply chain. This creates an immutable and auditable record that can be accessed by authorized parties, providing complete transparency and visibility into the entire supply chain.

IoT and edge network devices can be used to capture and transmit real-time data, such as temperature, humidity, location, and other relevant information. This data can then be stored on the blockchain, providing an accurate and trustworthy record of the goods' condition and location throughout the supply chain.

Tokens can be used to represent physical assets, such as products or raw materials, allowing for secure and transparent ownership and transfer. Smart contracts can be used to automate many of the processes within the supply chain, such as payments, deliveries, and quality assurance checks.

A cloud-based digital supply chain solution can provide a centralized platform for managing and monitoring the entire supply chain. This can help to streamline processes, reduce costs, and improve collaboration across different supply chain participants.

### Key Concepts

Before diving into the details of the use case, let's review some key concepts.

Blockchain: A blockchain is a distributed ledger that records all transactions in a secure and transparent manner. Each transaction is verified and recorded by multiple parties, making it tamper-proof.

### Smart Contracts: 
Smart contracts are self-executing contracts with the terms of the agreement between buyer and seller being directly written into lines of code. They allow for the automation of contract execution and enforceability, without the need for intermediaries.

### Edge Computing:
Edge computing is a distributed computing paradigm that brings computation and data storage closer to the location where it is needed, to improve response time and save bandwidth.

Now, let's move on to the use case.

**Use Case: Blockchain-Based Supply Chain Management System**

Let's consider a use case where a company manufactures and sells a variety of products to different customers. The company wants to ensure that its products are tracked throughout the entire supply chain, from manufacturing to delivery, to ensure timely delivery and quality control.

### Step 1: Product Creation and Registration

The first step in this use case is to create and register the products on the blockchain. The product information is stored in a smart contract, including its name, description, and unique identifier. The smart contract also includes a set of rules and conditions that must be met for the product to be considered valid.

### Step 2: Product Manufacturing and Inspection

Once the product is registered on the blockchain, the manufacturing process begins. During the manufacturing process, the product is inspected at various checkpoints to ensure quality control. Each inspection is recorded on the blockchain, along with the location, time, and inspector details.

### Step 3: Product Packaging and Shipment

After the product is manufactured and inspected, it is packaged and prepared for shipment. The package has an RFID tag containing a unique identifier linked to the product's smart contract on the blockchain. The RFID tag is scanned at each checkpoint during the shipping process, and the location and time are recorded on the blockchain.

### Step 4: Edge Computing and Data Processing

During the shipping process, edge computing comes into play. The data from the RFID tags is processed locally on the edge devices, reducing the amount of data that needs to be sent to the cloud. This ensures faster response times and reduced bandwidth requirements. The edge devices can be deployed on delivery trucks or at warehouses.

### Step 5: Delivery and Customer Feedback

Once the product is delivered to the customer, they can provide feedback on the product's quality and delivery. The feedback is recorded on the blockchain, and the smart contract is updated to reflect the delivery status. If the product meets the pre-defined conditions, the payment is automatically released to the manufacturer, eliminating the need for intermediaries.

### Benefits
Implementing a blockchain, IoT, edge network, tokens, smart contracts, and cloud-based digital supply chain solution offers many benefits for businesses, including:

#### Increased Transparency: 
With a blockchain-based supply chain, all participants can access a shared, immutable ledger that provides complete transparency into the entire supply chain. This can help to reduce the risk of fraud, counterfeiting, and other illegal activities, and increase consumer trust in the supply chain.

#### Improved Efficiency:
 Automation through smart contracts and real-time data from IoT and edge network devices can help to streamline processes and reduce delays and errors. This can lead to faster delivery times, lower costs and improved supply chain performance.

#### Enhanced Security:
 The use of blockchain technology ensures that data is secure and tamper-proof, reducing the risk of data breaches and cyberattacks. Additionally, the use of tokens and smart contracts can help to ensure that goods are transferred securely and that payments are processed automatically.

#### Increased Sustainability:
 By providing greater visibility into the supply chain, businesses can identify areas for improvement, such as reducing waste and emissions, improving energy efficiency, and sourcing sustainable materials.

### Conclusion
In conclusion, implementing a blockchain, IoT, edge network, tokens, smart contracts, and cloud-based digital supply chain solutions can offer significant benefits for businesses. By improving transparency, efficiency, security, and sustainability, companies can create a more robust and resilient supply chain ecosystem that can meet the needs of modern businesses. 




Blockchain for Supply Chain

# Harnessing the Power of Artificial Intelligence with Cloud Computing

## Introduction:
In today's fast-paced business landscape, organizations are constantly seeking ways to leverage artificial intelligence (AI) to gain a competitive edge. However, implementing AI solutions can be complex and resource-intensive, often requiring substantial computing power. This is where cloud computing comes into play, providing the scalability, flexibility, and cost-efficiency necessary for AI initiatives. At Technovature, we combine our expertise in AI and cloud computing to help businesses unlock the full potential of these technologies and deliver superior results.

### Business Pain Point:
Many businesses face significant challenges when it comes to implementing AI. Traditional on-premises infrastructure often falls short of meeting the demanding computing requirements of AI workloads. This can result in increased costs, delayed project timelines, and restricted scalability. Additionally, organizations may lack the necessary AI tools, frameworks, and libraries to develop and deploy AI models effectively.

### Clear Solution:
Cloud computing offers a compelling solution for AI implementation. By leveraging the power of the cloud, businesses can overcome infrastructure limitations and streamline their AI projects. At Technovature, we assist our clients in designing and implementing cloud-based AI solutions that are tailored to their specific needs. We help them migrate their AI workloads to leading cloud platforms such as Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP). This allows seamless scalability, high-performance computing, and access to a wide range of AI tools and services offered by these platforms.

Moreover, cloud providers offer specialized AI services, such as AWS SageMaker, Azure Machine Learning, and GCP's AI Platform, which provide pre-built models, automated machine learning, and deployment capabilities. These services simplify the AI development lifecycle and enable organizations to accelerate their AI initiatives.

### Technovature's Expertise:
At Technovature, we have a proven track record in implementing AI solutions on the cloud. Our team of experts possesses deep knowledge of AI algorithms, cloud architecture, and cloud-native development. By leveraging the power of cloud computing, we enable our clients to drive innovation, enhance decision-making processes, and deliver personalized customer experiences.

We work closely with our clients to understand their unique business requirements and develop customized AI solutions. Our expertise extends to leveraging cloud-native AI services, building AI models, developing intelligent chatbots, implementing computer vision solutions, and integrating AI capabilities into existing applications.

By partnering with Technovature, businesses can benefit from our knowledge leadership, extensive experience, and industry best practices, ensuring successful AI adoption and the realization of tangible business outcomes.

### Conclusion:
Cloud computing acts as the backbone for AI initiatives, providing the necessary infrastructure, scalability, and agility required for successful AI implementation. 

With Technovature's expertise in AI and cloud computing, organizations can overcome infrastructure challenges, streamline their AI projects, and achieve tangible business outcomes. By embracing AI on the cloud, businesses can stay ahead in today's data-driven world, drive innovation, enhance customer experiences, and gain a competitive edge.



Harnessing the Power of Artificial Intelligence with Cloud Computing

## The Cloud Native Computing Foundation
 

(CNCF) is a nonprofit organization committed to advancing the development of cloud native applications and services. Cloud native refers to applications and services that are designed to be deployed in a cloud environment and are optimized for modern infrastructure, such as containers, microservices and orchestration.

The CNCF was founded in 2015 by Google, IBM, Intel, Microsoft, Red Hat and VMware. The organization’s mission is to promote and advance the development of cloud native technologies and applications.

The CNCF is governed by a board of directors, which is responsible for setting the organization’s strategic direction and approving its annual budget. The board is composed of representatives from member companies, which include Google, IBM, Intel, Microsoft, Red Hat, VMware, as well as other leading technology companies.

The CNCF offers a variety of resources to help developers build cloud native applications and services. These resources include the Cloud Native Computing Foundation website, which features a variety of

 
## What is Cloud Native Computing?
 

Cloud Native Computing is an approach to software development that emphasizes the use of reusable components, rapid application development, and DevOps practices. It is designed to help organizations build applications that are both scalable and resilient.

Cloud Native Computing is based on the idea that applications should be broken down into small, modular pieces that can be quickly and easily assembled and redeployed. This approach allows organizations to quickly adapt to changing business conditions and to scale their applications up or down as needed.

DevOps is also a key part of Cloud Native Computing. DevOps helps organizations to develop and deploy applications more quickly and efficiently. By using DevOps practices, organizations can reduce the time it takes to go from code to production.

Cloud Native Computing is becoming an increasingly important approach to software development. Many organizations are now using Cloud Native Computing to build applications that are scalable and resilient.

 
## The benefits of Cloud Native Computing
 

Cloud Native Computing is an approach to application development and deployment that relies on containers to package and run applications. Containers are isolated from one another and the host operating system, which makes them well-suited for running on a public cloud infrastructure.

Adopting Cloud Native Computing can provide a number of benefits for your organization, including:

### 1.&nbsp;Increased Efficiency and Agility

Cloud Native Computing enables you to quickly and easily deploy new applications and updates. Containers can be spun up and down as needed, and new applications can be added without affecting the existing infrastructure. This increased efficiency and agility can help your organization keep up with the competition and respond to changing market conditions.

### 2.&nbsp;Improved Scalability

Cloud Native Computing also enables you to scale your applications more easily. As your business grows, you can add more resources to your applications without having to redesign them or redeploy them. This improved scalability can help you keep up with demand and avoid capacity bottlenecks.

 
## The Challenges of Cloud Native Computing
 

Cloud native computing is a term for software that is designed to run in the cloud. It is a term that is used to describe a new way of developing and deploying applications.

Applications that are designed for the cloud are typically broken into small pieces, or microservices. These microservices can be deployed independently and can be scaled up and down as needed.

The advantage of using cloud native applications is that they can be scaled up and down quickly and efficiently. This can help to reduce the cost of running applications in the cloud.

The challenges of using cloud native applications include the following

###1.&nbsp;A need to design applications for the cloud 

As cloud native promotes the use of a resilient Microservices application architecture, the redesign of an existing application into a Microservices architecture is a fairly complex and a non-trivial and could be a time-consuming experience.

### 2.&nbsp;A need to manage the infrastructure

Things like setting up the infrastructure, investing heavily in automating end-to-end, keeping the tools and the infrastructure up to date.

### 3.&nbsp;Right Skills and Staff

Finding the right skills and staff who can understand and build applications in a Cloud Native development environment.

### 4.&nbsp;Cost 

With complexity of Microservices and adoption of various cloud backing services, the bill of material for such infrastructure, component and services can sky rocket within no time, especially for the first time and if it has not been planned for.

 
## Cloud Native Computing in the Real World
 

Cloud Native Computing (CNC) is a term for software that is designed to take advantage of the features of the cloud computing platform. In other words, CNC software is software that is written specifically to run on a cloud platform.

There are several benefits of using CNC software. First, CNC software is designed to be scalable, meaning that it can easily handle increases in load. Additionally, CNC software is often more fault-tolerant than traditional software, meaning that it can tolerate more errors without crashing. Finally, CNC software is often easier to maintain and update than traditional software.

All of these benefits make CNC software a good choice for businesses that are looking to move to the cloud. In particular, CNC software can help businesses to reduce their IT costs and improve their performance.

 
## Conclusion
 

In conclusion, the Cloud Native Computing Foundation (CNCF) is a vital resource for anyone looking to adopt or implement cloud native computing technologies. By providing a range of resources, including training, certification, and support, the CNCF helps make the process easier and more efficient. Additionally, the CNCF is actively involved in the development of key cloud native technologies, such as Kubernetes and Prometheus, ensuring that these technologies continue to evolve and improve.

Cloud Native Computing Foundation

[#javascript](https://dev.to/t/javascript) [#gatsbyjs](https://dev.to/t/gatsbyjs) [#react](https://dev.to/t/react)

## JAM Stack

JAM stands for ***Javascript, API and Markup***, an acronym with a nested acronym. 🤓

![JAM Stack](/newImg/blog/jamstack/img1.png)


[jamstack.org](https://jamstack.org/) defines JAM as *modern web development architecture based on client-side JavaScript, reusable APIs, and pre-built Markup.*

Defining what JAM means doesn't really gives us enough depth though, is there any secret behind it?

Let's take a look at the file structure for an example JAM website:

![JAM Code](/newImg/blog/jamstack/jam-code.png)

As you can see, there's no magic behind it. Just a set of files that we're all used to seeing in the front-end aspects of our projects

Based on that file structure some could even try to argue that [Space Jam's website](https://www.warnerbros.com/archive/spacejam/movie/jam.htm) is a JAM site (spoiler: it isn't, it doesn't even use CSS 😂).

The diagram below outlines the differences between traditional web architecture and JAMstack web architecture.

![JAM Code](/newImg/blog/jamstack/jamstack01.jpg)

The key difference is the reduction in server-side complexity, removing the need for a tightly coupled Web Server and App Server, as well as the Database and/or Content Management System (CMS). Instead, the focus shifts to “client + services”, where the main web application can be distributed globally via a Content Delivery Network (CDN) and (where required) interacts with discrete services directly from the client via APIs.

The JAMstack web architecture is made possible, thanks to the advancements in web browsers, which today are comparable to full operating systems (e.g. [Chromium OS](https://www.chromium.org/chromium-os)), capable of interacting with countless APIs and running complex applications client-side. This, alongside the rapid growth of JavaScript front-end development (e.g. React) and comprehensive build tools (e.g. Gulp), can make traditional web architecture obsolete for certain scenarios.

The diagram below provides a deeper look at the JAMstack web architecture, highlighting the developer experience.

![JAM Code](/newImg/blog/jamstack/jamstack02.jpg)

Static site generators (e.g. Jekyll) and build tools (e.g. Gulp), provide a mechanism to automatically build web applications locally, producing a set of easy to cache static assets. These static assets can then be deployed via a simple “Git Push”, following a traditional Continuous Integration / Continuous Deployment methodology.

If we look at one example of a JAM website built using [Gatsby JS](https://www.gatsbyjs.org/), [Rachio Smart Sprinkler](https://rach.io/store/), we can see that for the end user, there are no immediate visual changes, this website is statically built and still offer full e-commerce and checkout journeys, with the help of APIs.

## Why should I give myself the trouble?

Even if the end user won't really have any immediate advantages, there are a number of reasons that will encourage you to consider the JAM approach on your next project. Let's visit some of them:

### Load speeds

**No database querying**: As your website's markup is built on build time, there is no database queries being done at every page request. This saves both on server load as well as overall response times.

**No template parsing**: Depending on your stack, chances are you have a template file in your code base that gets parsed and is used to build the HTML that gets sent back to the browser on every page request. With a static site that wouldn't happen, meaning you can also save on server loads and response time by taking this aspect out of the request/response flow.

### Separation of concerns

**Front-end takes care of all presentation**

With front-end frameworks increasing in popularity and capabilities, having your website/application's presentation decoupled from any backend framework means you can harness all the power that your front-end stack gives you, and in turn deliver a better experience to your end users.

**Centralised/focused UX design**

Equally relevant, if you don't need to worry about how the backend of your application handles its data and business logic, you can focus more of your efforts ensuring that your user experience is at the level you want and your end users expect, without have any potential constraints imposed by backend solutions.

### Frequency of content update

**How often do we really update ALL our pages?**

This is a point that is very often overlooked. For a number of our pages, content is rarely ever updated, eg contact pages, terms and conditions, blog posts.

You may update such pages a few times soon after they're published to fix potential typos or inconsistencies but once that period is gone chances are you will never edit that page again so why not have it statically built and save your server the hassle of having to compile this page on every request?

**Frequently changing content can use APIs**

If you have pages that still need to provide dynamic or time sensitive content, you can use APIs to update that content once the page has loaded and maybe cache that content on the client-side or (less optimal) include that content after the page has loaded.

### No need to implement everywhere

There is no need to move your entire application to JAM all at once.

**JAM Stack your blog**

As mentioned above, certain pages are less likely to change after they've been published so making them static would provide you with increased performance and better experience for end users.

**User authenticated areas could remain non-JAM'd**

If your application has some sort of authenticated area where things need to be kept dynamic, you can skip that section when *JAM'ing* your application.

**Progressive migration to JAM**

The takeaway from this section is that you can slowly migrate your content to a static build, and bring in more API backed dynamic content as you identify the opportunities in your application for a JAM approach.

### Tie in with service workers, SPA's and animated page transitions

JAM stack makes it easier for us to include new web workflows onto our applications, such as [Service Workers](https://developer.mozilla.org/en-US/docs/Web/API/Service_Worker_API), single page application frameworks and animated transitions between pages.

**Offline support**

With service workers you can make some or all of your website's content available to users when they're offline. This is a great feature, but specially important if your core market involves tourists or users with less than optimal internet connections.

**Push update to service workers on build time**

As I said earlier some pages would rarely change their content after being published, but when they do, you can use service workers to revalidate your content based on your needs. This will ensure your end users always have the most relevant content on their devices.

**Page transitions make UX sleek (like on native apps)**

This may not seem like a key feature of static and JAM based sites, but if you take a moment to think about how nice some native apps transitions between their different areas, it is really exciting to think that we can do the same on a web based application. See [Sarah Drasner](https://t.co/pr1NhYseRK)'s [page transitions](https://page-transitions.com/)example if you need some inspiration.

### Caching

Lastly, caching a statically built site is even more efficient than a dynamic one as you can cache all aspects of your site and depending on your content type your cache can be set to extremely long expiry dates.

For any portions of your site that need to be dynamic, you can use the Javascript and APIs aspect of JAM to pull in content after the page has loaded.

### Security

Static assets offer the highest level of security for web applications, due to the fact they do not require databases, plugins or execute dynamic code server-side. Any required server-side processes are abstracted into service APIs, called by the client, which significantly reduces the attack surface. Traditional web applications often require server-side plugins (e.g. Wordpress) to operate, which are extremely vulnerable and require continuous maintenance.

### Automation

Workflow is highly automated through the use of modern static site generators and build tools, with deployment occurring via a simple “Git Push”. This approach results in loose coupling and a clear separation of concerns, delivering a simplified development and debugging environment.

### Cost

Static assets are simple to host, as they do not require complex server-side configuration. This simplicity ensures a highly competitive market and therefore very aggressive pricing (even free).

## Caveats

As with most things in our area, there can also be trade-offs or at least things that you should keep in mind when crafting your JAM based application. I already touched on these above but for the purpose of having all of these under one section, a few caveats to consider when building JAM apps are:

**E-commerce and extremely time sensitive content**

If your content is extremely time sensitive and relies heavily on a backend solution, JAM may not be the best option. One way to circumvent this would be to use APIs to update the content on the front-end, but this may not be possible all the time.

**Must implement SSR**

There are many reasons behind this, but it's always recommended that you implement some sort of server side rendering logic at least at build time, for your content.

**Ensure no JS support if relevant and SEO best practices**

I know this may sound a bit dated, but implementing no JS support can be very relevant depending on your target audience or the percentage of users you want to deliver your application to. It's worth mentioning that no JS support could be slightly limited when compared to the full application, but it's important to give no JS users at least some sort of consistent experience.

Depending on the type of website or application you will also want to ensure that you're covering your grounds in terms of SEO. Having a great website could mean nothing if no one can find out about it.

**Authenticated areas of your app**

Authenticated areas of your site may not always be compatible with the JAM approach. Again, APIs can help you here, but it's up to you to make that call for each individual case.

A few suggestions on this would be to SSR the chrome of your site, and maybe add some placeholder markup to indicate that the user specific content is still being fetched with AJAX/APIs.

## How can I join the JAM Stack hype train?

Now that you have seen some pros and cons of JAM, you may want to try it out for yourself. There are plenty of ways you can build a JAM based website and most of these have some sort of static site feeling to them.

I compiled a list with some options you could try, but this is by no means a comprehensive list, just things that I came across or used myself in the past.

[**React JS**](https://reactjs.org/)

React is probably the most widely used front-end framework these days and if you know React you probably know all these options. Either way, these are a few tools that you can use to build a JAM stack:

- [create-react-app](https://github.com/facebook/create-react-app): Great zero config React application scaffolder with the possibility to *eject* your app once more custom features are needed.

- [GatsbyJS](https://www.gatsbyjs.org/): A static site generator for React with a built in GraphQL data layer and the ability to built static HTML from your React components on build time.

- [Next.js](https://nextjs.org/): A framework for server-rendered React applications with support for static sites.

[**Vue.js**](http://vuejs.org/)

Vue is also a front-end framework that has been increasing in popularity and adoption very quickly. If you're using Vue, these are a few of the tools you can use to build JAM apps:

- [Nuxt](https://nuxtjs.org/): Universal Vue JS application framework, often said to be the Vue equivalent of Next.js,

- [VuePress](https://vuepress.vuejs.org/): Static site generator for Vue.

- [eagle.js](https://github.com/Zulko/eagle.js): A hackable slideshow framework built with Vue.js (used to create my talk's slides 🤓).

**Other options**

- [Jekyll](https://jekyllrb.com/): Ruby based static site generator, widely used for [GitHub Pages](https://pages.github.com/).

- [Hugo](https://gohugo.io/): Go lang based static site generator.

- [StaticGen](https://www.staticgen.com/): StaticGen offers a list of static site generators that you can sort and filter based on your preference or needs.

- Build your own: [NIH syndrome](https://en.wikipedia.org/wiki/Not_invented_here) aside, if you have a very specific set of features that you require, you can always build your own static markup and integrate it with your APIs in a way that suits your business and needs.

## JAM friendly backend solutions?

One other important aspect of JAM stack is ensuring that your backend solutions integrate well with front-end frameworks and provide APIs that you can harness from the front-end/client-side of your application. A few examples of frameworks and services that can help you with that are:

- Headless WordPress: Using WordPress as your CMS doesn't necessarily mean you need to use it to generate your website's markup directly. API-first/Headless WordPress is something that is increasing in popularity and plugins and libraries to help you get started are out there.

- [Netlify CMS](https://www.netlifycms.org/): A file based CMS that can plugin in directly into your version control and build workflow.

- [Prerender](https://prerender.io/) : SEO friendly plugin to your application that ensures your website can be crawled correctly by search engines

- Your current framework of choice: This may not be as obvious for all CMS/frameworks but chances are you can already build a JAM website and integrate with your current backend solution, possibly by using REST and/or GraphQL.

## Conclusion

Thanks for reading my post. JAM stack is awesome and I believe it's the most efficient way forward for us. When you get to your next project, consider giving JAM stack a try or maybe consider rebuilding portions of your current set up using JAM, you won't be disappointed. 😉

What is JAM Stack

## What are Cloud Native Microservices- their setup and benefits?
 

Dividing application into discrete independent parts makes it greatly easier for the developers to craft agile and much more resilient applications. This approach also significantly lessens the development cycle, which translates into rapid innovations, faster executions, and an incredible user experience. 

These small but independent parts of cloud native applications are the cloud native microservices. The core of cloud native application architecture, the cloud native microservices is now the key tools used by the organizations making a move to the cloud.

With this article team Technovature throws light on the various aspects of cloud native microservices, and sums up their benefits while also elaborating their setup.

So let’s delve into the details!

 
## What are cloud native micro services? 
 

Cloud native microservices point the application design methodology in which the app is divided into the discreet, independent, workable units. Those units are called microservices.

The cloud native microservices arrange the application into the multiple independent services, with each one serving its own function. And, sharing a common data and network interaction, they enable the app to function properly- in unison!

Microservices and cloud architecture go hand-in-hand, because the cloud native applications are based on the microservice architecture. And, to the surprise cloud native microservice architecture have been around longer than cloud native computing.

Other advantage of microservices is that they don’t need to run them only in cloud environment. Using Kubernetes the microservices can also be deployed on premises.

 
## How to setup/build cloud native microservices?
 

Cloud native microservices can be implemented as an architectural style for the applications in myriads of manners. When one is ready to adopt microservice architecture then for the development or deployment the 3C’s are must to be followed-

a) Componentize
b) Collaborate
c) Connect

But with the benefits in mind, developers tend to code the microservice without a proper design plan. In cloud native microservices it is always crucial to plan first then code. The following 5 important steps regarding how to build microservices will help you setup & build efficient microservices.

### 1. Getting the right blueprint
Directly starting the microservice development with code might land you into troubles. Creating a single service is an easier task but that’s not the requirement. The final function of all the microservices is to deliver a functional application in the aggregation.

The first step should be in regards with the defining the application functionality then identify the individual microservice requirements. 

### 2. Anti-pattern
The system designs mostly interpret the structure of the organization designing them. But the same must not be followed in case of microservices. In place of mirroring the organizational structure investigate the microservice designs in the light of functionality to be delivered in the new architecture.

### 3. Operational Support
Apart from the functionality, as mentioned in the first two points, operational support for the new architecture needs to be ensured accordingly. Tracking transient resources and maintaining a history of application metrics, one can easily perform time-series assessments.

### 4. Performance & Resilience
Each microservice is required to deliver high performance and continued operations even in the instances of resource failures. Therefore it is crucial to address the risk potentialities as early as possible. Preparing the microservices for load variations and lack of resources will ensure the application’s robust performance against the real-world-challenges!

### 5. Future Planning
The applications tend to develop and evolve fast, and new features are rolled out in the latest versions. With the continuous deployment ventures the microservice applications are continually developing.

So the microservice must be designed in a way to conform to the desiderata and empower the updates fast & easy.

 
## What are the Benefits of using Microservices in a Cloud-Native architecture?
 

Developing the complete application as a single monolithic structure creates several troubles for the connectedness and the dependency of all units with each other. Shifting away from this pattern and implemented cloud native microservices in cloud native application designs render several benefits.

Let’s talk about them individually-

### 1. Enhanced Productivity
Breaking down an application into miniscule fragments is much easier to develop & maintain. Each of the service can be independently managed in any of the programming language across different software environment, as per the needs.

### 2. Greater Resilience
The microservice-based architecture eases up the process of investigating and resolving the issues. Individual modules facilitate specific fault isolation and the larger application remain impervious to that single failure.

### 3. More scalability
The polyglot nature of the cloud native infrastructure allows any programming language to execute the microservice without any issue of incompatibility. With microservice architecture any service can be scaled individually and the new components can be added without downtime or redeployment.

### 4. Optimized Functionality
When the focus is on individual service in lieu of the entire application then the components can be customized as per the need and improved functionality. Workability of each module separately helps teams focus on capabilities rather than technologies.

### 5. CI/CD
DevOps philosophy and Agile development approach both are centred around CI/CD. The cloud native microservices empower the cross-functional teams to craft, check, deploy, and update the services independently. This leads to the faster deployments and troubleshooting.

 
## How Technovature can help?
 

* Understand yout Legacy Application architecture and the Landscape
* Understand your Business needs like what you want to deliver to your customer? things like superior user experience, salability, resiliency etc.
* Work with you on a migration plan to build a Microservices blueprint for your organization.
* Help you in developing a poly architecture, full stack microservices and make it ready for prime time.

 
## Final Words
 

Microservices and cloud architecture are thoroughly discussed in the article and the light has also been thrown on how to build Microservices, as well!

Cloud native microservices greatly help the developers in rolling out the new features fast and reliably. Feel free to write what you liked in the article or the additions that can improve the knowledge resource. Constructive criticism will also be cordially accepted.

 
## Frequently Asked Questions
 

### 1. Are micro services for cloud-native apps?
Mainly they are used for cloud native apps, but with Kubernetes they can also be used in on-premise applications.

### 2. What are the benefits of using microservices in a cloud-native architecture?
Better productivity, enhanced resilience, better scalability, optimized functionality, continuous improvement and continuous development are the major benefits rendered by the microservices.

### 3. Can we develop cloud native microservices using Apache camel?
With Instana- the automated APM, cloud native microservices can be developed with Apache Camel. 

### 4. Why is microservices architecture better compared to others?
Microservices allow division of application into small autonomous discrete components that can be operated individually, without disturbing the application as a whole. This empowers the developers with several competitive edges.

### 5. How to develop cloud native applications with microservices architecture?
The cloud native microservices arrange the application into the multiple independent services, with each one serving its own function. And, sharing a common data and network interaction, they enable the app to function properly- in unison!

Microservices Development | Technovature

# De-Risking Traditional Banking using Blockchain

## Introduction
We recently saw the collapse of some of the biggest banks such as Silicon Valley Bank (SVB), Signature Bank and immense pressure on smaller regional banks in the North America. 

While the 2008 crisis was all about credit related subprime crisis, this time we saw extreme drop in yields from long term securities held by banks against customer deposits that demand high and some time volatile liquidity for vaious dynamic financial situations putting immense pressure on balance sheets and forcing them to book losses.

Here we discuss how Traditional Banking could adopt innovation and next generation technologies in order to diversify, derisk and eventually strenghthen their balance sheets.

Blockchain-powered decentralized finance (DeFi) and fintech platforms have the potential to offer several advantages over traditional banking, particularly in terms of liquidity, credit, and long-term low yield treasuries. Here are a few ways in which DeFi and fintech can be more powerful than traditional banking and make banking business less risky:

**Liquidity**: DeFi and fintech platforms allow for greater liquidity than traditional banks by enabling peer-to-peer transactions and removing the need for intermediaries. This can help reduce transaction costs and increase the speed of transactions.

**Credit**: DeFi and fintech platforms can offer more inclusive and accessible credit options than traditional banks, particularly for those who may not have access to traditional credit systems. This can help stimulate economic growth and increase financial inclusion.

**Long-term low yield treasuries**: DeFi and fintech platforms offer alternative investment options that can provide higher returns than traditional long-term low yield treasuries. This can be particularly attractive for investors looking for more diversified investment options and can help reduce risk in portfolios.

**Risk management:** DeFi and fintech platforms can help mitigate risk by providing more transparent and secure transaction processing. The decentralized nature of these platforms reduces the risk of a single point of failure or cyber attack, making them more secure than centralized systems.

**Innovation**: DeFi and fintech platforms are more agile and innovative than traditional banks, which can help them adapt more quickly to changing market conditions and customer needs. This can help reduce risk by enabling banks to stay ahead of the curve and respond to changing customer demands.

Overall, DeFi and fintech platforms powered by blockchain technology have the potential to make banking business less risky by offering greater liquidity, more accessible credit options, higher returns on investment, better risk management, and more innovation. As these technologies continue to evolve, they are likely to become increasingly important to the future of banking and finance.

## How to increase profitability?

Banks are facing a range of challenges that are impacting their profitability, including losses in their balance sheets and increasing interest rates set by the Federal Reserve. However, blockchain technology has the potential to help banks overcome some of these challenges and maintain profitability. Here are a few ways blockchain can help:

### Streamlining processes: 
Blockchain can help banks streamline their processes and reduce costs by automating tasks and eliminating intermediaries. For example, blockchain can be used to automate back-office processes such as clearing and settlement, reducing the need for manual intervention and lowering operational costs.
Improving security: Blockchain provides a secure and tamper-proof ledger that can help protect against fraud and cyber attacks. By using blockchain to securely store and share data, banks can reduce the risk of data breaches and protect customer information.

### Enhancing transparency: 
Blockchain provides a transparent and immutable record of all transactions, which can help improve transparency and accountability in the banking industry. This can help build trust with customers and regulators, which can ultimately help maintain profitability.

### Reducing costs: 
By eliminating intermediaries and automating processes, blockchain can help banks reduce costs and increase efficiency. This can help banks maintain profitability in the face of increasing interest rates and other financial challenges.

### Enabling new business models:
 Blockchain can also enable new business models for banks, such as the use of cryptocurrency and digital assets. By leveraging blockchain technology, banks can tap into new revenue streams and potentially reach new customers.

## How to adopt?

Traditional banks can adopt DeFi and blockchain technologies in a number of ways to improve their business model and reduce risk. Here are a few examples:

### Leveraging blockchain for secure data management: 
Blockchain technology can provide secure, tamper-proof data management for banks, reducing the risk of fraud or hacking. Banks can use blockchain to store and share customer data securely and transparently, making it easier to prevent and detect fraudulent activity.

### Implementing smart contracts for automated processes: 
Smart contracts are self-executing contracts with the terms of the agreement between buyer and seller being directly written into lines of code. Banks can use smart contracts to automate processes such as loan origination, trade finance, and payment processing, reducing the risk of human error and improving efficiency.

### Offering cryptocurrency services: 
Banks can offer cryptocurrency services to customers, allowing them to buy, sell, and hold cryptocurrencies directly through the bank. This can provide customers with a more convenient and secure way to invest in cryptocurrencies, while also generating additional revenue for the bank.

### Partnering with DeFi platforms: 
Traditional banks can partner with DeFi platforms to offer customers a wider range of investment options, such as yield farming or decentralized exchanges. This can help banks stay competitive and attract new customers, while also providing a way to diversify their revenue streams.

###Exploring digital identity solutions:
 Blockchain technology can also be used to develop secure and reliable digital identity solutions, reducing the risk of identity fraud and improving customer onboarding and authentication processes.

### Conclusion
By adopting these next-generation technologies, traditional banks can improve their risk management processes, reduce costs, and provide customers with new and innovative services. However, it is important for banks to carefully consider the potential risks and challenges associated with these technologies and to implement them in a responsible and secure way.








De-Risk Banking

# Leveraging Open-Source Serverless Technology for Cloud-Native Kubernetes

### Introduction:
Cloud-native architectures have revolutionized the way we design and deploy applications, providing flexibility, scalability, and resiliency. However, with the increasing complexity of distributed systems, the need for serverless technology has become crucial. Serverless computing allows developers to write and deploy code without worrying about the underlying infrastructure, providing a more efficient and cost-effective solution. In this article, we will explore the benefits of using open-source serverless technology with Kubernetes and how Technovature can help you achieve this.

#### The Benefits of Serverless Technology for Kubernetes:
Serverless technology can help overcome some of the challenges of traditional Kubernetes architecture. Here are some of the key benefits:

##### Reduced Overhead: 
By eliminating the need to manage and maintain infrastructure, serverless technology allows developers to focus on writing code and delivering features faster.

##### Increased Scalability:
Serverless technology allows for the automatic scaling of resources based on demand, providing greater flexibility and cost savings.

##### Reduced Costs: 
With serverless technology, you only pay for the resources you use, making it more cost effective than traditional Kubernetes architecture.

##### Improved Resiliency:
Serverless technology provides built-in failover mechanisms that ensure your application remains available even during failures.

#### Open-Source Serverless Solutions for Kubernetes:
There are several open-source serverless solutions available for Kubernetes, including Knative, OpenFaaS, and Kubeless. These solutions provide a powerful and flexible platform for deploying serverless functions within Kubernetes, allowing developers to build and deploy applications with ease.

**Knative**: Knative is a Kubernetes-based platform that provides a set of building blocks for deploying and managing modern serverless workloads. It offers a wide range of features, including automatic scaling, event-driven workflows, and built-in monitoring and logging.

**OpenFaaS**: OpenFaaS is an open-source serverless platform that runs on top of Kubernetes. It provides a powerful and flexible platform for deploying and scaling serverless functions, and it can be easily integrated with existing workflows and tools.

**Kubeless**: Kubeless is another open-source serverless platform that runs on Kubernetes. It provides a simple and easy-to-use platform for deploying and scaling serverless functions, and it supports a wide range of languages and frameworks.

### Serverless Use Case
Imagine a company that provides an online e-commerce platform. During peak holiday seasons, the company experiences a huge surge in traffic which results in higher server loads, longer response times, and a potential risk of downtime. The company needs to scale its infrastructure to handle the increased traffic, but investing in additional servers for just a few weeks of the year is not cost-effective.

This is where Serverless comes in. By using a Serverless platform like OpenFaaS, the company can deploy its application as a series of stateless functions that automatically scale based on the incoming traffic. This means the company can handle the peak holiday season traffic without investing in additional servers or worrying about server management.

In addition, Serverless allows the company to only pay for the actual usage of its functions, rather than for the idle server time. This helps the company to save costs and allocate its resources more efficiently.

Overall, Serverless helps solve the important business problem of handling unpredictable traffic spikes while also providing cost-effective infrastructure management.

### How Technovature Can Help:
At Technovature, we have a team of experts who can help you leverage the benefits of open-source serverless technology for your Kubernetes deployment. Our team has extensive experience in designing and deploying cloud-native architectures, and we can help you choose the right serverless solution for your needs. We can also help you with migration, integration, and ongoing support to ensure your deployment is efficient, scalable, and resilient.

### Conclusion:
Serverless technology provides a powerful and flexible platform for deploying and scaling applications within Kubernetes. With the rise of open-source serverless solutions, it has become easier than ever to leverage the benefits of serverless computing. At Technovature, we can help you design, deploy, and manage your serverless Kubernetes deployment, providing you with a cost-effective and efficient solution for your cloud-native architecture.

Serverless Computing

# Cloud Native and Distributed Computing: Evolving and Thriving in the Kubernetes Era

In the age of cloud computing, the way we build, deploy, and manage software applications has undergone a dramatic shift. With the adoption of microservices and containerization, we have moved from monolithic architectures to more dynamic and scalable distributed systems. This shift has brought about new challenges, but also new opportunities. In this article, we will explore how cloud-native and distributed computing are evolving and thriving in the Kubernetes era.

### What is Cloud Native Computing?

Cloud Native Computing is a term used to describe a set of principles and practices that enable organizations to build and run scalable and resilient applications in modern cloud environments. At the core of this approach is the use of microservices architecture and containerization. These technologies allow applications to be broken down into small, independent services that can be scaled up or down depending on demand. Containerization enables the packaging and deployment of these services in a consistent and reproducible manner, while also providing isolation and security.

### The Rise of Kubernetes

Kubernetes is an open-source platform that automates the deployment, scaling, and management of containerized applications. It has quickly become the de-facto standard for managing containerized applications and is widely adopted in cloud-native architectures. With Kubernetes, developers can deploy and manage microservices-based applications with ease, while operations teams can ensure high availability, scalability, and reliability.

### Distributed Computing in the Kubernetes Era

Distributed computing has been around for decades, but with the rise of cloud-native architectures and Kubernetes, it has taken on a new level of importance. The use of microservices and containers allows organizations to distribute workloads across multiple machines and scale them up or down dynamically based on demand. This approach brings about several benefits, including increased fault tolerance, improved scalability, and better resource utilization.

One of the key benefits of distributed computing in the Kubernetes era is the ability to build resilient and fault-tolerant applications. By distributing workloads across multiple machines, organizations can ensure that if one machine fails, the application will continue to run without interruption. Kubernetes provides several features that enable organizations to build and run fault-tolerant applications, including automated failover, self-healing, and automatic scaling.

### The Technovature Advantage

At Technovature, we are fully tuned in with the Cloud Native Computing Foundation (CNCF) and are actively contributing to various CNCF projects. Our expertise in Kubernetes and distributed computing allows us to help organizations design, develop, and deploy cloud-native applications that are scalable, reliable, and secure.

We understand the challenges that come with distributed computing, and we work with our clients to build applications that can handle the complexities of a distributed environment. Our approach is to focus on building resilient and fault-tolerant applications that can withstand failures and provide high availability.

Use Case: Implementing Cloud-Native and Distributed Computing Principles

One example of how cloud-native and distributed computing principles can be implemented is in the context of a large e-commerce application. In this scenario, the application needs to be able to handle millions of requests per day, with peaks during major sales events. By using microservices and containerization, the application can be broken down into smaller, independent services that can be scaled up or down as needed. Kubernetes can be used to manage the deployment and scaling of these services, ensuring high availability and fault tolerance.

In addition, by leveraging distributed computing principles, the application can be designed to handle failures without impacting the end user experience. For example, if one server fails, the application can automatically switch to a backup server, ensuring that the user experience remains seamless.

#### Conclusion
In conclusion, distributed computing is a rapidly evolving field that has seen significant advancements in recent years. With the increasing demand for cloud-native architectures and the proliferation of Kubernetes, distributed computing has become an essential tool for organizations seeking to build scalable and resilient systems.

Technovature is at the forefront of this evolution, leveraging its expertise in cloud-native technologies and distributed computing to deliver innovative solutions to its clients. By following the latest developments in the CNCF and contributing to various projects, Technovature is committed to staying ahead of the curve and providing cutting-edge solutions that meet the evolving needs of its clients.

As businesses continue to embrace digital transformation and shift to cloud-based infrastructures, distributed computing will only become more critical. With its deep expertise and commitment to innovation, Technovature is well-positioned to help organizations navigate this rapidly changing landscape and unlock the full potential of distributed computing.





Distributed Cloud Computing

# Safeguarding Data in the Age of Cybersecurity and Cloud Computing

### Introduction:
In the digital age, the increasing frequency and sophistication of cyber threats have made cybersecurity and data privacy top priorities for businesses. Protecting sensitive information has become crucial to maintaining customer trust and complying with regulatory requirements. Cloud computing offers robust security features and seamless data management, making it an integral component of a comprehensive cybersecurity strategy. At Technovature, we leverage cloud-native solutions to help businesses safeguard their data effectively, mitigate cyber risks, and deliver superior results.

### Business Pain Point:
Traditional cybersecurity measures often fall short in addressing the dynamic and evolving nature of cyber threats. Inadequate protection can result in data breaches, financial losses, reputational damage, and regulatory non-compliance. Managing data security in complex on-premises infrastructures can be challenging, resource-intensive, and costly for businesses, potentially leading to vulnerabilities and compliance issues.

### Clear Solution:
Cloud-native computing offers a range of security benefits for organizations. By embracing cloud computing, businesses can strengthen their security posture and simplify data management. Technovature assists organizations in leveraging leading cloud platforms such as Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP) to implement robust security measures.

Cloud providers offer a wide array of security services, including encryption, identity and access management (IAM), threat detection and response, data loss prevention (DLP), and security information and event management (SIEM). These services enable organizations to automate security processes, monitor threats in real time, and swiftly respond to incidents, reducing the risk of data breaches.

Technovature works closely with clients to develop customized security strategies aligned with their unique business requirements. Our experts conduct comprehensive security assessments to identify vulnerabilities and design robust security architectures. We assist in developing incident response plans, implementing advanced threat detection systems, and ensuring compliance with regulatory standards such as GDPR, HIPAA, or PCI DSS.

### Technovature's Expertise:
Technovature specializes in implementing cloud-native security solutions tailored to our client's specific needs. Our team of cybersecurity experts combines their knowledge of cloud computing with industry-leading security practices to help organizations safeguard their data effectively. We have expertise in designing and implementing secure cloud architectures, conducting vulnerability assessments, implementing encryption technologies, and developing security policies and procedures.

By partnering with Technovature, businesses gain access to our knowledge leadership, extensive experience, and the latest security best practices. We provide ongoing support to ensure the robustness of security measures, conduct regular audits, and address emerging threats.

### Conclusion:
Cloud computing acts as a catalyst for effective cybersecurity, providing businesses with scalable, secure, and resilient infrastructure. With Technovature's expertise in cloud-native security solutions, organizations can strengthen their security posture, mitigate cyber risks effectively, and ensure compliance with industry regulations. By adopting cloud-native cybersecurity solutions, businesses can gain peace of mind, focus on their core competencies, and protect their valuable data and customer trust.

Safeguarding Data in the Age of Cybersecurity and Cloud Computing

Cloud Native Capacity Planning for Kubernetes

### Introduction

As organizations adopt cloud-native architectures, Kubernetes has become the de facto standard for container orchestration. Kubernetes allows for easy scaling and deployment of containers, but it also requires careful capacity planning to ensure optimal performance and cost-efficiency.

### Problem Statement

Capacity planning in a cloud-native environment can be challenging due to the dynamic nature of container workloads. Traditional capacity planning techniques that rely on static infrastructure and predictable workloads are not effective in Kubernetes environments.

In a Kubernetes environment, containers can be added or removed dynamically based on application demand, making it difficult to predict the resources needed for optimal performance. In addition, Kubernetes allows for horizontal scaling of containers, which can result in increased costs if not done carefully.

### Solution: Cloud Native Capacity Planning

To effectively plan capacity in a Kubernetes environment, organizations need to adopt cloud-native capacity planning techniques that take into account the dynamic nature of container workloads.

One approach is to use auto-scaling techniques that allow Kubernetes to automatically adjust the number of containers based on application demand. This can help ensure optimal performance while also minimizing costs by scaling down containers during periods of low demand.

Another approach is to use monitoring and analytics tools that provide real-time insights into container performance and resource usage. These tools can help identify bottlenecks and inefficiencies in the Kubernetes environment, allowing organizations to make data-driven decisions to optimize capacity and performance.

Finally, adopting a DevOps culture that emphasizes continuous optimization can help organizations stay on top of capacity planning and performance optimization in a Kubernetes environment. By continuously monitoring and optimizing their Kubernetes environment, organizations can ensure optimal performance and cost efficiency.

### Technovature Value Proposition

At Technovature, we specialize in helping organizations adopt cloud-native architectures and Kubernetes. Our team of experts can help you develop a cloud-native capacity planning strategy that takes into account the dynamic nature of container workloads, while also optimizing performance and cost-efficiency.

With our deep expertise in Kubernetes and cloud-native architectures, we can help you adopt auto-scaling techniques and monitoring tools to ensure optimal performance and capacity planning in your Kubernetes environment. We can also help you adopt a DevOps culture that emphasizes continuous optimization, so you can stay on top of capacity planning and performance optimization.

₹₹₹ Benefits

By adopting a cloud-native capacity planning strategy, organizations can:

Ensure optimal performance and cost-efficiency in their Kubernetes environment
Leverage auto-scaling techniques and monitoring tools to adjust container resources dynamically based on application demand
Identify and address bottlenecks and inefficiencies in the Kubernetes environment
Adopt a DevOps culture that emphasizes continuous optimization to stay on top of capacity planning and performance optimization.
Conclusion

Capacity planning is critical in a Kubernetes environment to ensure optimal performance and cost-efficiency. By adopting cloud-native capacity planning techniques, organizations can take advantage of the dynamic nature of container workloads in a Kubernetes environment while also optimizing performance and cost-efficiency.

At Technovature, we have the expertise to help you adopt a cloud-native capacity planning strategy that takes into account the dynamic nature of container workloads, while also optimizing performance and cost-efficiency. Contact us today to learn more about our Kubernetes and cloud-native architecture services.


Effective Cloud Finops

## What are cloud native application and their competitive edges? 
 

Cloud Native Application Development has gained a tremendous momentum in the last few couple of years, and now [75% of the global organizations](https://venturebeat.com/data-infrastructure/report-75-of-companies-are-focusing-on-cloud-native-apps/#:~:text=Growth%20in%20cloud%2Dnative%20workloads,up%20from%2030%25%20in%202021.) are focussing on developing cloud native application architecture. 
But haven’t you wondered, what are the reasons behind such a humungous popularity of the cloud native application design? 
Well, firstly the pressing need for rapid digitalization in the pandemic period called for the agile and robust development tools & technologies. Though the work was in pipeline but the greatest impetus came in that era. Moreover, the incredible leading edges made the cloud native application development much more popular. 
In the year 2021, only 30% of the digital workloads were deployed through the cloud native application architecture, But [Gartner predicts ](https://www.gartner.com/en/newsroom/press-releases/2021-11-10-gartner-says-cloud-will-be-the-centerpiece-of-new-digital-experiences)that by 2025, with this pace, the percentage will enhance to a whopping 95%! 
With this article team Technovature throws light on various aspects of the cloud native application architecture and elaborate the benefits leading to its greater popularity. So, let’s delve into the details. 

 
## What are cloud native applications?
 

In the simplest terms, cloud native applications are the programs designed for the cloud computing architecture. 
Cloud native application designs are developed, executed, and hosted in cloud and are designed to leverage the intrinsic characteristics of software delivery model over cloud computing. These applications use micro-service architecture, which makes them much more efficient. And this is because such architecture efficiently allocates the necessary resources for each service. This makes the application more flexible and highly adaptable to cloud architecture. 

 
How to architect & design Cloud Native Applications
 

The principle of cloud native architecture centres attention towards optimizing the system architecture for the special capabilities of cloud computing. Traditional architecture tends to optimize the high-end infrastructure, requiring a heavy load of manual efforts. Therefore only a small number of components are worked on in it. 
While in the cloud a fixed infrastructure makes lesser sense because cloud services are charged on the usage basis and reducing the presence means lesser expense. Easier automation and scalability are other perks. So we can conclude that cloud native architecture takes care of accomplishing resilience through horizontal scaling, parallel processing, and automated replacement of the failed components. 
Now let’s specifically check out the cloud native application architecture

 
## Cloud Native Application Architecture
 

Cloud native is about where the application is developed & deployed, not where put on!
And cloud native architecture is a design practice, which utilizing the cloud services like AWS, AZURE, EC2, S3, allows the development techniques for agile and dynamic application development. This methodology empowers the developers with a modular approach to craft, run, and update software through a set of cloud-based micro-services. 
Micro-services and containerization bolster the agility and dynamism cloud native application development by facilitating the easier transfer between the service providers and deploying the services independently in different scripts or frameworks without any conflict. 

 
## Cloud Native Application Design
 

Cloud native application designs are based on following 8 main principles:

### 1. Loosely coupled micro-services
To develop a single application as a suite of many small services, each running their process and communicating with light protocols. 

### 2. Omni-language & Framework support
To let the developer teams utilize the script they master and leverage the sporadic expertise at all nooks & corner of the world. 

### 3. APIs for Interaction & Collaboration 
To leverage protocol based lightweight API for exposing the functionality. Internal communication within the system occur using binary protocol.

### 4. Stateless but high scalability
To make the system impervious to any external entity making a change by storing state in database form. Architecture must be able to scale up by simple addition of server nodes.

###5. Resilience 
To respond to the failures by successfully avoiding data loss or downtime

###6. Orchestrated Lightweight Containers 
To isolate applications into small execution environments that shares the operating system kernel 

###7. CI/CD for Agile DevOps and Automation 
To empower the developers deliver code changes frequently and reliably, and establish a consistent & automated process to develop, assemble, and test applications. 

###8. Dynamic Scalability 
To leverage cloud’s flexibility in using increased or decreased resources using demand spike or slow down. 

 
##Traditional vs. Cloud Native Application
 

<div style="width:100%; overflow-x: auto">
<table style='text-align:center; ' width='100%' border='1'>
<tr>
<th style="padding:10px;">###S.No</th>
<th style="padding:10px;">###Context</th>
<th style="padding:10px;">###Cloud Native</th>
<th style="padding:10px;">###Traditional</th>
</tr>

 <tr>
 <td style="padding:10px;">1.</td>
 <td style="padding:10px;">OS Abstraction</td>
 <td style="padding:10px;">With PaaS developers need not fret about the layer management of the operating system, as it is delegated to the cloud service provider</td>
 <td style="padding:10px;">Traditional software development is completely coupled with the underlying operating system, making it harder to migrate and scale over different hosting environments.</td>
 </tr>

 <tr>
 <td style="padding:10px;">2.</td>
 <td style="padding:10px;">Modularity</td>
 <td style="padding:10px;">Micro services and containers impart a very high modularity. For this the development & maintenance processes need not to be performed on the whole app but on independent parts.</td>
 <td style="padding:10px;">Services and functionality are coupled with the application deployment. There is a modular structure in development aspects and deployed as monoliths.</td>
 </tr>

 <tr>
 <td style="padding:10px;">3.</td>
 <td style="padding:10px;">Management Cost</td>
 <td style="padding:10px;">For the high modularity improvements, scaling and testing cost is relatively lower.</td>
 <td style="padding:10px;">For the monolith model improvement, scaling and testing is not only expensive but also harder.</td>
 </tr>

 <tr>
 <td style="padding:10px;">4.</td>
 <td style="padding:10px;">Resource Utilization</td>
 <td style="padding:10px;">For the dynamic allocation of the resources based on the current needs, resources are efficiently utilized.</td>
 <td style="padding:10px;">Resources in a fixed amount need to be allocated in advance.</td>
 </tr>

 <tr>
 <td style="padding:10px;">5.</td>
 <td style="padding:10px;">Collaboration</td>
 <td style="padding:10px;">Team collaboration is oriented to the DevOps principles and agile development method, leading to closer collaboration.</td>
 <td style="padding:10px;">The waterfall model leads to not so high collaboration among developers. Velocity and quality to the release cycle is lesser.</td>
 </tr>

 <tr>
 <td style="padding:10px;">6.</td>
 <td style="padding:10px;">Automation </td>
 <td style="padding:10px;">Built using CI/CD over a series of automated processes the overall procedure in app life-cycle is highly automated.</td>
 <td style="padding:10px;">Built using manual operation approach the automation level is lower in traditional app development approach.</td>
 </tr>

</table>
</div>

 
##Cloud Native Application Protection Platform- CNAPP
 

CNAPP is a cloud native platform security model encompassing cloud security posture management, Cloud service network security, and also the cloud workload Protection Platform in one overarching platform.

It emphasizes the need for the businesses to ponder on cloud native platform security solutions. This way, in place of patchwork of tools, a complete holistic security to the applications can be provided.

 
##Benefits of Cloud Native Applications
 

The aforementioned dissection would have already concluded a lot of benefits of the cloud native applications. Now let’s have a look specifically.


### 1. Independent Scalability
All micro services are isolable, and independently scalable- without affecting other components.

### 2. High Portability
As there are containers to port micro-services among different infrastructure, these applications are vendor-neutral and avoid vendor-lock-in.

### 3. Greater Reliability
For using containers, the cloud native applications do not affect adjacent services in case of failure in one micro service.

### 4. Easier Management
Automated deployment and updates along with traceability & management of each micro service independently makes the overall management a cakewalk, comparatively.

### 5. Greater visibility
The micro service architecture isolating the services makes it easier for the developers to look out & study everything easily.

### 6. Cost-Effectiveness
Here, as the computing and storage resources can be scaled up or down with needs, the elimination of overprovisioning greatly reduces the operational cost.

 
##Frequently Asked Questions
 

For a quick roundup or to get a glimpse of what you were thinking in regards with Cloud Native Applications, let’s have a nippy look at the rapid-fire question! 

### 1. What is cloud native development?
 It is a methodology to develop, run, and update apps in the distributed computing facilitated by the cloud delivery model.

### 2. What are the characteristics of cloud native applications?
Discrete, reusable components (micro-services) working in unison and able to integrate in any cloud environment and the dynamic orchestration of computing, storing, and networking resources are the major attributes.

### 3. Which practices are used while building a cloud native application?
Micro services, lightweight containers, automated release pipeline, provision resource with IaaC, server less development, are some practices followed in building a cloud native application.

### 4. Can cloud native applications access local file systems?
Special CSI drivers can facilitate the cloud native application access the local storage files.

### 5. Do cloud native applications use micro services architecture?
Yes, micro services are the core of cloud native applications and a key tools for businesses planning to go cloud.

Cloud Native Application

## What are some of the challenges associated with big data?
 

Big data is a term used to describe the large and ever-growing volume of data that businesses and organizations must manage. The challenges associated with big data include:

### 1. The sheer volume of data

 The amount of data being generated and collected is constantly increasing, and businesses must find ways to manage and store this data.

### 2. The variety of data

Data can come in many different formats, including text, images, and video. Businesses must find ways to effectively manage and analyze all of this data.

### 3. The velocity of data

Data is being generated and collected at an increasingly rapid pace, and businesses must find ways to keep up.

### 4. The veracity of data

Much of the data that businesses must manage is unstructured and incomplete. Businesses must find ways to make sense of this data and to trust it.

### 5. The value of data

With so much data available, it can be difficult sometimes to differentiate what is good and relevant/meaningful data and which is not.

Big data has been a buzzword for a few years now, and it seems to be only gaining more traction. Many companies are scrambling to find ways to store, manage, and analyze the data they are collecting. But big data is not without its challenges. Here are some of the biggest ones.

### 6. Managing the data

One of the biggest challenges with big data is managing it. With so much data coming in, it can be difficult to store it all and keep track of it at such high volume.

In this modern world, data is everything. It is a valuable commodity that businesses use to make informed decisions and stay ahead of the competition. But with the ever-growing volume of data, comes new and increasingly complex challenges.

How do you manage and make sense of all this data? How can you ensure that you are getting the most value from it? These are just some of the questions that businesses are grappling with in the era of big data.

One of the biggest challenges is managing the data deluge. With more and more data being generated every day, it can be difficult to keep track of it all. The volume of data is growing faster than our ability to process it.

This is where big data tools and technologies come in. They help you to manage and process large volumes of data more efficiently. But even with these tools, it can be a challenge to keep up with the data deluge.

### 7. Extracting value from Data

The term "Big Data" is used to describe the large and ever-growing volume of data that businesses are collecting. This data can be used to improve decision-making, but extracting value from it can be a challenge.

There are several ways to extract value from Big Data. One way is to use data mining techniques to find patterns in the data. Data mining can be used to identify customer trends, discover new products, and predict customer behavior.

Another way to extract value from Big Data is to use it to improve business processes. For example, businesses can use data to improve their supply chain operations or to optimize their marketing campaigns.

Big Data can also be used to improve decision-making. By analyzing data, businesses can make better decisions about where to allocate their resources, what products to offer, and how to price their products.

The challenge of extracting value from Big Data can be overcome by using the right tools and techniques.


 
## Conclusion
 

Big data is a term for data sets that are so large or complex that traditional data processing applications are inadequate. Challenges of big data include the need for more sophisticated analytics, data management, data governance and data security.

Despite the challenges, big data can provide valuable insights that can help organizations improve their business performance. The key is to have the right tools and processes in place to take advantage of big data insights.

Challenges with Big Data

As the world moves towards a more decentralized future, the emergence of Web 3.0 and blockchain technologies is transforming the way we interact and transact online. At Technovature, we are at the forefront of this revolution, helping businesses and organizations leverage the full potential of these cutting-edge technologies to build decentralized applications that are more secure, transparent, and efficient than ever before.

## Introduction:
Decentralized finance (DeFi) is a rapidly growing sector in the blockchain industry, offering financial services that are open, accessible, and transparent to anyone with an internet connection. A key component of DeFi is the decentralized application (Dapp), which enables users to interact with blockchain-based smart contracts and access financial services without intermediaries. In this article, we'll explore an end-to-end real world example of a Dapp in the context of DeFi.

### Dapp Example:
Let's consider a hypothetical DeFi Dapp called "TokenSwap" that allows users to swap tokens on the Ethereum blockchain. TokenSwap is built using the Ethereum Virtual Machine (EVM), which enables the execution of smart contracts on the Ethereum network. TokenSwap is also integrated with a decentralized exchange (DEX) protocol called Uniswap, which is used to determine the exchange rates for token swaps.

### Technical Architecture:
TokenSwap's technical architecture consists of several components that work together to enable token swaps. Here's a brief overview of the components:

**Smart Contract - **TokenSwap's smart contract is deployed on the Ethereum network and contains the business logic for token swaps. The smart contract is written in Solidity, a programming language used for developing smart contracts on the Ethereum network.

**Web3.js **- TokenSwap's front-end is built using Web3.js, a JavaScript library that enables interaction with the Ethereum network via the JSON-RPC API.

**Wallet** - To use TokenSwap, users need to have an Ethereum wallet that can interact with the Ethereum network. Popular options include MetaMask and MyEtherWallet.

**Uniswap** - TokenSwap is integrated with Uniswap, a decentralized exchange protocol that enables the determination of exchange rates for token swaps. TokenSwap uses Uniswap's smart contracts to determine the exchange rate for each token swap.

**Gas Fees - **To execute transactions on the Ethereum network, users need to pay gas fees in Ether. These gas fees are used to compensate miners for validating and executing transactions on the network.

**Oracles** - Oracles are used to provide off-chain data to the smart contract. In TokenSwap's case, oracles are used to provide exchange rates for tokens that are not supported by Uniswap.
TokenSwap's workflow is as follows:

User selects the token they want to swap and the token they want to receive.
TokenSwap checks Uniswap's smart contract for the exchange rate between the two tokens.

TokenSwap calculates the exchange rate based on the Uniswap rate and executes the swap transaction via the smart contract.
TokenSwap deducts the gas fee from the user's wallet and pays it to the miners.
Tokens are swapped and the user's wallet is updated with the new token balance.

### Benefits:
Using a Dapp like TokenSwap provides several benefits over traditional financial services. Here are a few:

**Trustless** - Dapps are built on blockchain technology, which provides transparency and immutability. This means that users can trust the smart contract to execute transactions without intermediaries.

**Decentralized** - Dapps are not controlled by a central authority, which means that they are open and accessible to anyone with an internet connection.
Lower Fees - Dapps typically have lower fees than traditional financial services since they don't have intermediaries to pay.

### Our Technical Expertise

At Technovature, we have a team of highly skilled and experienced developers who specialize in Web 3.0 and blockchain technologies. Our technical expertise includes:

Developing decentralized applications (dApps) on blockchain platforms such as Ethereum, Hyperledger, and Corda
Building smart contracts using Solidity and other programming languages
Implementing blockchain-based identity and authentication solutions
Integrating Web 3.0 technologies such as IPFS and Swarm into dApps
Developing blockchain-based supply chain management solutions
Implementing decentralized finance (DeFi) applications such as automated market makers (AMMs) and lending platforms
With our deep understanding of these technologies and our hands-on experience in developing blockchain-based solutions, we are able to provide our clients with unparalleled technical expertise and guidance.

### Our Value Proposition

At Technovature, we believe that Web 3.0 and blockchain technologies have the potential to transform a wide range of industries, from finance and healthcare to supply chain management and beyond. By leveraging our expertise in these areas, we help our clients:

- Improve data security and privacy by implementing blockchain-based solutions that are resistant to hacking and fraud

- Increase transparency and accountability by creating decentralized systems that are more resistant to censorship and control
- Streamline processes and reduce costs by eliminating intermediaries and automating complex workflows
- Unlock new business models and revenue streams by leveraging the power of decentralized finance and tokenization

In addition to our technical expertise and industry knowledge, we also pride ourselves on our collaborative approach to working with clients. We see ourselves as partners in our clients' success, and we work closely with them to understand their needs and develop customized solutions that meet their specific requirements.

### Our Branding

At Technovature, we are committed to providing our clients with the highest quality services and solutions. Our branding reflects this commitment, with a focus on innovation, excellence, and customer satisfaction. We see ourselves as leaders in the Web 3.0 and blockchain space, and our branding reflects this leadership position.

### Conclusion

At Technovature, we are passionate about helping our clients unlock the full potential of Web 3.0 and blockchain technologies. With our technical expertise, value proposition, and collaborative approach, we are able to provide our clients with cutting-edge solutions that are tailored to their specific needs. Whether you are looking to build a decentralized application, implement a blockchain-based solution, or explore the possibilities of decentralized finance, we are here to help.


