Data Wrangling and Exploration using Pyspark
“Data wrangling is not just about cleaning; it’s about structuring data to make it usable and insightful for analysis.”
This article demonstrates the use of PySpark for loading, transforming, and exploring retail banking data from Kaggle. Using Databricks notebooks, the author establishes relationships between datasets, like linking client and account information, while showcasing a scalable approach to data wrangling in the context of a data lakehouse design.