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data
A warehouse schema that answers questions fast
Model facts and dimensions, then watch a slow query become instant.
Why it matters
Analytics engineering is a growing role and star schemas are its core skill. It also teaches you why normalising everything is the wrong default for reads.
What you build
- Fact and dimension tables
- Slowly changing dimensions handled deliberately
- Queries measured before and after
- Documentation of every grain
If you want more
- Add incremental fact loading
- Add a semantic layer
Build it
One row equals what, exactly. Get this wrong and everything downstream is wrong.
- Build the dimension tables
Material for the whole build
Shows you can design for the read pattern, which is what separates an analytics schema from a copied application one.
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