Data Warehouses on AWS

Build cloud-based data warehouses that power analytical workloads. Learn dimensional modeling techniques—including star and snowflake schemas, fact grain, and surrogate keys—to structure data for efficient OLAP queries. Use Python and SQL to build ETL pipelines that extract from diverse source systems like PostgreSQL, Cassandra, and Neo4j, clean and conform data across sources, and load it into Amazon Redshift. Optimize table performance with distribution styles, sort keys, and compression to speed up queries at scale. Create materialized views that pre-compute common aggregations so analysts get fast answers without recalculating. Validate data quality to ensure your warehouse is accurate, complete, and production-ready.