An ETL pipeline using Pyspark for a Bank
“While notebooks are great for exploring and gaining an initial understanding of the data, they are not versatile enough for building end-to-end data pipelines.”
This article demonstrates building an automated ETL pipeline using PySpark in PyCharm for a bank. It uses Kaggle’s Retail Banking Demo Data and highlights the transition from initial data exploration in Databricks notebooks to creating a structured, scalable data pipeline.