Data is the driving force behind many of today’s businesses. With the ever-growing amounts of data available, businesses need to create optimized data pipelines that can handle large volumes of data in a reliable and efficient manner. In this article, we will discuss how to create an optimized data pipeline using PySpark and load the data into a Redshift database table. We will also cover data cleansing, transformation, partitioning, and data quality validation.
Before diving into the code, let’s take a quick look at the tools we will be using: