Explains why transactional data models are inefficient for analytics and how to design denormalized, query-optimized models for better performance.
Explains database denormalization: when to flatten data for faster analytics queries and when to avoid it.
An introduction to data modeling concepts, covering OLTP vs OLAP systems, normalization, and common schema designs for data engineering.
Compares columnar vs. row-based data structures, explaining their optimal use in OLAP and OLTP systems for performance and scalability.
An introduction to modern data systems, explaining OLTP, OLAP, data warehouses, data lakes, and the roles of data engineers, analysts, and scientists.
Explains the differences between batch and streaming data processing, covering OLTP, OLAP, and ETL concepts for data engineers.
