Data Modelling for Startups: Domain Models & the Modern Data Stack
summary
A fast path for startup analytics is to centralize raw sources into one warehouse, model using a domain-aligned main model (source → domain → presentation), and expose a curated presentation layer so dashboards and self-serve explorers don’t diverge. Typical stack: Airbyte for ingestion, Snowflake (or BigQuery) for storage, dbt for transformations, Metabase/Looker for output, Elementary for tests and HighTouch/Census for reverse ETL. Agency experience: ~40 clients in 5 years, use CI/CD on GitHub and run tests on every PR to avoid model chaos. No separate pipelines per tool.
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chapters (4)
- 0:00Start
- 9:44Pitfall Reinventing the Wheel
- 18:38Pitfall 2 Data Model Chaos
- 28:12Pitfall 3 Sad Stakeholders
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