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

Why the Stack Exists at All

Every analytics system solves the same underlying problem. Data is born scattered, messy, and structurally inconsistent. A production database, a sensor feed, and a spreadsheet an analyst maintains by hand share almost nothing in common technically. The data analytics stack exists specifically to move raw, heterogeneous data through a series of transformations until it becomes something a human or a model can actually reason about. This first article covers the foundation. Where data originates, how it gets captured, and how it gets stored once captured.