Data Modelling, Warehousing & the Modern Data Stack is a structured track of 12 lessons that build a complete, interview-ready understanding of the topic. Work through them in order, then use the quiz and flashcards in each lesson to revise.
What this course covers
- How Analytics Data Is Stored: Databases, Warehouses & Lakes - Each storage type, what it optimises for, and when analytics uses which.
- Transactional versus Analytical Processing - Why the database running the app is the wrong place to run your analysis.
- Dimensional Modelling: Facts, Dimensions & Choosing the Grain - Fact and dimension tables, and why declaring the grain first prevents most bugs.
- Star Schema versus Snowflake Schema: Choosing a Design - Both designs compared on query speed, storage and maintenance.
- Slowly Changing Dimensions & Keeping History - How to track an attribute that changes over time without corrupting past reports.
- Extract-Transform-Load versus Extract-Load-Transform Pipelines - The two pipeline shapes, and why the warehouse era flipped the order.
- The Modern Data Stack: Ingestion, Storage, Transformation & Serving - The four layers of a current stack and the tool categories in each.
- Analytics Engineering: Transformation, Testing & Documented Models - Version-controlled transformation, model tests and documentation as part of the job.
- The Semantic Layer & Metrics Store: One Definition of Truth - Why governed metric definitions became critical once tools started answering questions.
- Data Quality Testing, Freshness & Pipeline Monitoring - The tests that catch a broken pipeline before a stakeholder does.
- Event Tracking & Instrumentation: Clean Data at Source - Naming conventions, event schemas and why most data problems start here.
- Case Study: Designing a Warehouse Model for a Delivery Business - A full dimensional model designed from a business brief, grain upwards.