Understanding Databricks: A Foundation for ML Engineers-1

You’re an ML engineer. You’ve worked with data in CSV files, pandas DataFrames, maybe PostgreSQL databases. You’ve trained models locally, tracked experiments in spreadsheets, deployed to Flask APIs. It works, but it doesn’t scale.

Databricks offers something different — a unified platform where data engineering, machine learning, and deployment converge. But the terminology is new. Delta Lake. Unity Catalog. Workspaces. Catalogs. What are these things? How do they fit together?

This is your foundation — the core Databricks objects an ML engineer needs to understand to use the platform to its full potential.

The Databricks Architecture: Three Layers

Think of Databricks as three interconnected layers:

1. Storage Layer: Where your data lives (Delta Lake)
2. Governance Layer: How you organize and control access (Unity Catalog)
3. Compute Layer: Where you process data and train models (Clusters, Notebooks, Jobs)

Understanding each layer and how they interact unlocks Databricks’ power.

Delta Lake: The Storage Foundation