Reference — 5 patterns

Data Architecture Patterns

Recurring solutions to recurring problems in data architecture, catalogued the way patterns should be: what each one intends, how it's structured, what it honestly costs, and when not to reach for it. Each entry links to the essays that examine it in depth. The catalog grows over time.

Pattern

Change Data Capture Ingestion

Replicate operational data by streaming the database's own change log, instead of repeatedly querying tables for what's new.

Pattern

Data Vault

Model the integration layer as hubs (business keys), links (relationships), and satellites (history), so the warehouse absorbs change without remodelling.

Pattern

Lakehouse

Keep one copy of data as open-format tables on cheap object storage, and let every engine — SQL, Spark, ML — read and write it with warehouse-grade guarantees.

Pattern

Medallion Architecture

Organise a lakehouse into layers of increasing refinement and trust: bronze (raw), silver (cleaned), gold (consumable).

Pattern

Star Schema

Model analytical data as central fact tables of measurements surrounded by flat, denormalized dimension tables of context.