Stage 2 · Lesson 8 of 9

Kimball, data vault and one big table

There are several styles for modelling a warehouse, and you should recognise them. builds a for each business process, such as sales or returns. The stars share , common ones such as customer and date, so numbers line up.

Inmon builds one company-wide warehouse first, then builds smaller marts from it. Data vault splits data into hubs (, such as a customer number), links (relationships between them) and satellites (details that change).

It suits many changing sources and a full audit history, but it needs more tables and tooling. One big table is a single wide table per use case, popular because dashboards read it fast. You only need to know, at a basic level, what problem each style solves.

Common trappicking a style because it is fashionable: a small team with five sources rarely needs data vault.

A question to check yourself, an AI practice session and 1 cheat sheet entry come with the full roadmap. Unlock

Resources for this stage

Start with

CourseFree
SQL Tutorial for Data Analysis (formerly Mode)
ThoughtSpot · Beginner
Its advanced section covers window functions, performance tuning and pivoting on realistic data

Practise

LeetCode SQL 50
Fifty graded problems; the window function and aggregation ones match what pipeline SQL needs
Select Star SQL
An interactive book on one real dataset that ends with window functions and self joins

Keep handy

Kimball Dimensional Modeling Techniques
The original definitions of grain, conformed dimensions and SCD types, straight from the source
roadmap.sh: SQL
Check which SQL topics you still lack before moving on

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Data Warehouse Design: Schemas, Steps, and Best Practices (opens in a new tab)Read
Star and snowflake schemas, facts and dimensions, and the steps of a warehouse design in one read