Data Modelling, ETL & Data Warehousing for Analysts

Why Analysts Need a Data Warehouse

Define Grain, Keys and Table Relationships

Normalise Operational Data Before Designing Reports

Build a Star Schema Around a Business Process

Choose Fact Grains and Aggregate Measures Safely

Model Dates and Reporting Calendars

Extract Source Data into an Auditable Staging Layer

Transform Staged Data into Consistent Records

Load Data Safely and Make Reruns Idempotent

Process Incremental Changes and Late Arrivals

Preserve Customer History with Slowly Changing Dimensions

Test Data Quality and Reconcile Every Load

Publish Analyst Marts with Consistent Metric Definitions

Read Warehouse Queries with Performance and Cost in Mind

Operate Pipelines with Dependencies, Lineage and Safe Access

Capstone: Build and Audit a Trusted Retail Sales Warehouse