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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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R Language Tutorial for Mainframe Programmers

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Why R? It's free, open source, powerful and highly extensible. "You have a lot of prepackaged stuff that's already available, so you're standing on the shoulders of giants," Google's chief economist told The New York Times back in 2009.

Free Resources on R Language

Details of R Language

Because it's a programmable environment that uses command-line scripting, you can store a series of complex data-analysis steps in R. That lets you re-use your analysis work on similar data more easily than if you were using a point-and-click interface, notes Hadley Wickham, author of several popular R packages and chief scientist with RStudio.

That also makes it easier for others to validate research results and check your work for errors -- an issue that cropped up in the news recently after an Excel coding error was among several flaws found in an influential economics analysis report known as Reinhart/Rogoff.

Why not R

Well, R can appear daunting at first. That's often because R syntax is different from that of many other languages, not necessarily because it's any more difficult than others.

How R is different from Excel

  • The R Language is different from Excel. In R you can use complex problems. Multiple sources of data you can do analyze in R Language.
  • In Excel, the capability of handling data sources is limited.
  • Connectivity to modern visualization tools like Tableau is cumbersome in Excel.

Where to download R-Free version

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