Databricks Architecture & Governance

Databricks is easy to start with and harder to run as an enterprise platform. This hub collects PRODCOB's Databricks writing in reading order: lakehouse fundamentals first, then governance with Unity Catalog, then the pipeline and loading patterns that decide whether the platform can be trusted in production.

Lakehouse · Unity Catalog · pipelines · incremental loading · Parquet

Start here

The platform and the operating model behind it.

Fundamentals
Begin with Databricks: Lakehouse Fundamentals Explained
Why Databricks is better understood as an operating model for data and AI than as another analytics tool, from Delta tables to MLflow and streaming.

Governance

What the platform can enforce, and where your own controls take over.

Architecture
Unity Catalog: Overrated or Misunderstood?
What Unity Catalog should enforce as a governance control plane, and what remains outside its control boundary.

Pipelines and loading

From working notebook to a pipeline the business can depend on.

Architecture
Databricks Pipelines: Beyond the Notebook
A notebook that works has proven the logic, not that the enterprise can depend on it: state, late data and transactional recovery.
Back2Front
Full Load vs Incremental Load: Azure Data Factory + Azure Databricks Quick Study Guide
Full load starts over; incremental load remembers where it left off. That memory makes it efficient and creates the need for stronger controls.

Storage formats

The file format underneath lakehouse tables.

Fundamentals
Apache Parquet Explained: Columnar Storage, Compression & Analytics
How columnar storage lets analytical engines read only the columns a query needs, and why that suits large datasets.

Coming next

Articles in preparation. Not yet published.

Planned
Auto Loader
Incremental file ingestion into the lakehouse.
Planned
Delta Lake
The transactional table format underneath Databricks tables.
Planned
Lakeflow
Databricks' approach to ingestion, pipelines and orchestration.