This is to explain Terraform for each looping technique. In this example, 3 buckets are created to demonstrate the looping idea.

In the first step, we will create the above 3 buckets starting from 0.

In this post, Spark reads the data from a CSV file to a DateFrame and saves that DataFrame as a Redshift table.

In addition to that, I’ve explained how to create a table in Postgres, use Jupyter magics and plot a diagram.

This is the continuation of the Spark Streaming Basics. I explained the basic stream example, which runs only on one AWS Glue container. The stream producer was Netcat, and the sink was a text file. In this post, the stream producer is still Netcat, but the sink is Kafka. Both Kafka and Spark running on Docker containers.

This is a very basic example created to explain Spark streaming. Spark run on the AWS Glue container locally.

This is a short description of lambda calculus. Lambda calculus is the smallest programming language that is capable of variable substitution and a single function definition scheme. Haskell is the functional programming language based on lambda calculus, which I will explore. I already explained how to use VSCode for Haskell Development to support the code listed here.