This is Part 2 of a three-part series on the modern data analytics stack. Part 1 covered sources, ingestion, and storage. Part 2 covers the data processing layer itself, transformation, and modeling. Part 3 will cover visualization, deployment, and orchestration.

Where Raw Data Becomes Useful Data

Part 1 of this series ended with data sitting in a warehouse, stored and queryable but still essentially raw. The data processing layer is where that raw material gets shaped into something an analyst, a model, or a dashboard can actually consume. This article goes deep into the specific engineering behind Spark, dbt, Pandas, and the analysis tools that sit downstream of them.