Learn dimensional modeling for BI: facts, dimensions and grain, building a star-schema SQLite warehouse, BI-grade SQL with window functions, and a small semantic layer that stops metric drift.
Level
02
Modules
05
Lessons
21
Introduction
Why Model Data for BI
Facts, Dimensions and Grain
Downloadable Assets and Resources
Star Schema vs Raw Tables
Surrogate Keys and Slowly Changing Dimensions
Assessment
A spreadsheet of KPIs is only as trustworthy as the data model behind it, and most businesses never build one: every report queries the raw transactional tables directly, and every report ends up computing "revenue" or "active customer" slightly differently. This course teaches the fix that real BI systems use: dimensional modeling. You design and build a small star-schema data warehouse for the fictional retailer Trailhead Outfitters in SQLite, write the SQL that reporting tools actually rely on, and wrap the core metrics in a lightweight semantic layer so a number means the same thing everywhere it is used. It is the second course of the Business Intelligence path, and the warehouse you build here becomes the shared foundation the rest of the path's dashboards and reports read from.
Every lesson follows the same rhythm: a short, real situation, a precise explanation next to a plain-words version, a code demo in Python and SQL that you run yourself, a few exercises with hints, and a summary of what to remember. The printed output in every lesson comes from running the code and the SQL against the real Trailhead data, so what you see on the page is what you get on your own screen. Everything runs offline in SQLite through Python's standard library; nothing needs installing beyond a free notebook environment, and no account, key or payment is required anywhere in the course.
You continue on the same fictional business, the outdoor gear retailer Trailhead Outfitters, using the customer, product and order data from Business Intelligence Fundamentals. The warehouse you design and load in this course's project becomes the data source the rest of the Business Intelligence path builds dashboards and automated reports on top of.
Short lessons that move from dimensional modeling fundamentals, through building and validating a star-schema warehouse, to writing reporting SQL and a semantic layer, plus a closing guided project. You also get the Trailhead source data, starter and solution notebooks with self-checks for the project, and a quiz drawn from a question bank at the end of every module.
If you already know some SQL and pandas but have only ever queried raw, messy tables, this course teaches you why real BI teams model data differently, and gives you the exact vocabulary and skills to do it yourself. It suits analysts who finished Business Intelligence Fundamentals and want to build the structures behind the KPIs they already know how to calculate, and it suits anyone who has been told to "just query the warehouse" and wants to understand what that warehouse actually is. By the end you will have designed, built and validated a real star-schema data warehouse, something you can describe in an interview or adapt directly to your own team's data. Enroll now, open the first lesson, and stop building reports on raw tables that quietly disagree with each other.
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