Learn how to optimize SQL queries and improve data warehouse performance through effective schema design, query optimization, and hardware-aware tuning. This course helps you understand how modern data warehouses process analytical workloads and how to make them more efficient.

Designed for learners interested in SQL performance tuning, data warehousing, and analytical query optimization, the course explores modern warehouse architecture, star and snowflake schemas, cost-based query optimizers, database statistics, cardinality, and selectivity. You'll apply advanced dimensional SQL, evaluate machine-learning-driven tuning and workload management, and examine how CPU caches, memory access, NUMA architectures, and parallel processing influence query performance. You'll also explore hardware-accelerated query processing using GPUs, FPGAs, and RDMA. By the end of the course, you'll be able to evaluate warehouse designs, analyze query optimization strategies, improve query-plan selection, and identify techniques for faster analytical workloads. What makes this course distinctive is its end-to-end approach, connecting SQL optimization, dimensional modeling, intelligent tuning, and hardware acceleration to help you make informed data warehouse performance decisions.