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How to Build CI/CD Pipeline: GitHub to AWS

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 Creating a CI/CD pipeline to deploy a project from GitHub to AWS can be done using various AWS services like AWS CodePipeline, AWS CodeBuild, and optionally AWS CodeDeploy or Amazon ECS for application deployment. Below is a high-level guide on how to set up a basic GitHub to AWS pipeline: Prerequisites AWS Account : Ensure access to the AWS account with the necessary permissions. GitHub Repository : Have your application code hosted on GitHub. IAM Roles : Create necessary IAM roles with permissions to interact with AWS services (e.g., CodePipeline, CodeBuild, S3, ECS, etc.). AWS CLI : Install and configure the AWS CLI for easier management of services. Step 1: Create an S3 Bucket for Artifacts AWS CodePipeline requires an S3 bucket to store artifacts (builds, deployments, etc.). Go to the S3 service in the AWS Management Console. Create a new bucket, ensuring it has a unique name. Note the bucket name for later use. Step 2: Set Up AWS CodeBuild CodeBuild will handle the build proces
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Exclusive Apache Kafka Top Features

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Here are the top features of Kafka. It works on the principle of publishing messages. It routes real-time information to consumers far faster. Also, it connects heterogeneous applications by sending messages among them. Here the prime component (a.k.a message router) is a broker. The top features you can read here.


Kafka features


The exclusive Kafka features

The message broker provides seamless integration, but there are two collateral objectives: the first is to not block the producers and the second is to not let the producers know who the final consumers are.

Apache Kafka is a real-time publish-subscribe solution messaging system: open source, distributed, partitioned, replicated, commit-log based with a publish-subscribe schema. Its main characteristics are as follows:

1. Distributed. Cluster


Centric design that supports the distribution of the messages over the cluster members, maintaining the semantics. So you can grow the cluster horizontally without downtime.

2. Multiclient.


Easy integration with different clients from different platforms: Java, .NET, PHP, Ruby, Python, etc.

3. Persistent.


You cannot afford any data lost. Kafka is designed with efficient O(1), so data structures provide constant time performance no matter the data size.

4. Real time.


The messages produced are immediately seen by consumer threads; these are the basis of the systems called complex event processing (CEP).

5. Very high throughput.


As we mentioned, all the technologies in the stack are designed to work in commodity hardware. Kafka can handle hundreds of read and write operations per second from a large number of clients.


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