Data Warehousing & OLAP

By the end, you can

• Distinguish OLTP from OLAP by access pattern, schema shape, and storage orientation.
• Explain why analytics should run on a separate data warehouse instead of production.
• Design a star schema with a fact table and dimension tables, and identify which columns belong where.
• Describe the difference between a star schema and a snowflake schema.
• Apply the five OLAP cube operations — roll-up, drill-down, slice, dice, and pivot — and explain what each does to the data.
• Explain why columnar storage is efficient for aggregate analytics queries.
• Trace data through an ETL (or ELT) pipeline from source systems to the warehouse.
• Define data mart, data lake, and lakehouse, and name the major cloud warehouse platforms.