Data Modeling Best Practices
Best practices for modeling analytics data, checked against the Kimball Group's dimensional modeling techniques, Microsoft's dimensional modeling guidance and the dbt documentation. It covers the grain, fact and dimension tables, slowly changing dimensions, dbt marts and naming, tests and contracts, and defining metrics once in a semantic layer.
7
Sefism · Oct 2026
26
Sefism · Oct 2026
8
Sefism · Oct 2026
Data and ML
Sefism · Oct 2026
Best practices
Other
Sefism
18 min read
Intermediate
Free
What this best practices guide is
Best practices for modeling analytics data, checked against the Kimball Group's dimensional modeling techniques, Microsoft's dimensional modeling guidance and the dbt documentation. It covers the grain, fact and dimension tables, slowly changing dimensions, dbt marts and naming, tests and contracts, and defining metrics once in a semantic layer.
It has 7 chapters and 26 practices, each with why it matters, what to do and what to avoid. For analytics and data engineers modeling a warehouse.
Chapter by chapter
1. Start from the business process and the grain (4 practices)
Every good dimensional model starts with two decisions: which business process you are modeling and what one row means. These practices make both explicit.
2. Fact tables (4 practices)
Fact tables hold the measurements of a business process. These practices cover picking the right type of fact table, keeping measures summable and keeping keys clean.
3. Dimension tables (4 practices)
Dimensions give facts their meaning: the who, what, where and when that people filter and group by. These practices keep them stable, shared and easy to use.
4. Slowly changing dimensions (4 practices)
Customers move, products change category, salespeople change region. These practices decide which changes rewrite history and which keep it.
5. dbt layers and naming (3 practices)
Names are the interface of a model. These practices, from dbt's structure and style guides, make models easy to find, read and join.
6. Tests and contracts (4 practices)
A model other people depend on is an interface. These practices test its logic and lock its shape so changes do not break consumers by surprise.
7. Metrics and the semantic layer (3 practices)
When every dashboard computes revenue its own way, the numbers drift apart. These practices define entities, dimensions and metrics once, next to the models.
How it works on Sefism
The guide reads like a short course: a syllabus, then one chapter per page in a focused reader, with a quiz at the end of each chapter for members. The opening chapters are open to everyone. Adding it to your account uses credits, opens every chapter and keeps your progress.
Every claim is checked against the sources listed on the page (8 cited).
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