Data Modelling Showdown: Unravelling the Mysteries of Different Data Modelling Approaches
summary
Compares concrete modelling tradeoffs and practical recipes: Data Vault: hubs links satellites loads concurrently and simplifies source integration but yields many joins so querying needs heavy indexing and often performs poorly, star schema faster is the default for BI and Power BI, snowflake cuts redundancy at cost of joins, entity‑centric tables speed ML use cases but force full reloads, one big table saves storage but costs query compute, recommends mixed models and BEAM (3Ds and 7Ws) to turn concepts into iterated data products and reuse about 50% reuse of common patterns.
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chapters (11)
- 0:00Welcome
- 3:20Why data modeling matters
- 9:01Data modeling concepts
- 11:10Data Vault modeling
- 14:44Graph and dimensional modeling
- 19:19Logical vs physical models
- 23:10BEAM and Sun modeling
- 29:41Reusing modeling patterns
- 33:13Data products value
- 41:48From concepts to insights
- 42:39Q&A
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