The databases that run an app are built to record things quickly. The databases that answer a company's questions are built to read a lot of history quickly. A data warehouse is the second kind: a central place where data from many sources is stored in a clean, consistent shape, ready for reports and analysis.
You'll start with what a warehouse is and how it differs from a regular database, using the contrast between OLTP (day-to-day transactions) and OLAP (analysis). Then you'll learn dimensional modelling, the standard way to organise warehouse data into fact tables and dimension tables arranged as a star schema, so that analysts can query it easily.
Good modelling makes every later step simpler, from pipelines to dashboards. Once you've finished this module, move on to Pipelines & Orchestration.
An introduction to data warehousing concepts and architecture, data marts, and how a warehouse differs from data mining.
How the two kinds of systems differ across the key criteria, and when each one is the better choice.
An introduction to dimensional modelling and star schemas, how they compare with entity-relationship models, and why they suit reporting and analytics.
A full beginner course on warehouse concepts and types, popular tools, how data flows through a warehouse, data marts, and data modelling.