We talk a lot about “data-driven decisions”, but that usually hides three separate layers:
- Data itself (events, transactions, logs, etc.).
- Database structure (schemas, constraints, relationships).
- Insights on top (from SQL, AI copilots, BI tools, notebooks).
My current interest is in that middle layer: using real-world database structures as a playground to practice database insights:
- Understanding and improving data quality (missing constraints, odd cardinalities, misuse of types).
- Suggesting schema improvements for future analytics (indexes, normalization vs denormalization, slowly changing dimensions, etc.).
- Making access easier via views, semantic layers, or documentation (ERDs, db docs, column descriptions).
I’m looking for good open source repositories that contain:
- Non-trivial database schemas (preferably SQL migrations or DBML, not just ORM models).
- Some realistic sample data if possible (to check cardinalities, null patterns, etc.).
I’m collecting good open-source schemas to practice database insights; here’s what I’m looking for and I’d love your suggestions.
- Do you know specific GitHub repos with interesting database schemas (preferably with migrations + demo data) that are good for practicing:
- data quality checks,
- schema critique,
- documentation and visualization?
- Have you tried using AI tools on top of real schemas to propose constraints, rename columns, or design better access patterns? How did that go?
Drop any links or examples you have – especially repos where the schema itself is a central part of the project.
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