Structure data so it is correct to write and fast to analyze. Relational modeling and normalization for OLTP, why analytics needs a different shape, dimensional modeling with star and snowflake schemas, slowly changing dimensions, the modern columnar cloud warehouse, and the data lake and lakehouse with ELT.
Before you start
You should be comfortable with SQL — the PostgreSQL course is a solid prerequisite. Examples use standard SQL DDL that runs on any warehouse.
Relational Modeling & Normalization
Model an operational database that keeps data correct — entities and relationships, primary and foreign keys, and normalizing to third normal form to kill the update anomalies that corrupt data.
OLTP vs OLAP
Operational and analytical workloads pull a schema in opposite directions. Understand why you separate them, and why analytics databases store data by column instead of by row.
Dimensional Modeling & the Star Schema
The star schema is how analytics data is shaped: one fact table of measurements surrounded by dimension tables of context. Learn facts vs dimensions, grain, and star vs snowflake.
Slowly Changing Dimensions
Dimension attributes change — a customer moves, a product is recategorized. How you handle that history (SCD Type 1, 2, or 3) decides whether your historical reports stay correct.
The Modern Cloud Warehouse
Snowflake, BigQuery, and Redshift changed data engineering by separating storage from compute and scanning columnar data in parallel. Learn what makes them fast and how to model for them.
Data Lakes & the Lakehouse
Not all data fits neatly in a warehouse. Learn the data lake, the file formats that make it queryable, the lakehouse table formats (Delta, Iceberg) that add reliability, and how the medallion architecture organizes raw to refined.