What you'll learn

Modern data platforms rely on distributed query engines to process massive datasets efficiently. Understanding how these engines read data, plan queries, and execute tasks in parallel is essential for designing fast, cost-effective analytics. In this course, Big Data Foundations: Query Engines and Analytics, you’ll gain the ability to explain and reason about how modern query engines work under the hood. First, you’ll explore how distributed query engines access data stored in object storage, table formats, and distributed file systems, including how they build logical and physical plans to execute SQL at scale. Next, you’ll discover major execution models and optimization techniques such as predicate pushdown, partition pruning, column pruning, caching, and adaptive execution, supported by hands-on Spark SQL demos. Finally, you’ll learn how analysts access and analyze big data using SQL interfaces, notebooks, BI tools, semantic layers, and metadata catalogs, and understand the trade-offs around performance, cost, and concurrency. When you’re finished with this course, you’ll have the foundational knowledge of big-data query engines and analytics needed to confidently navigate modern data lakes, warehouses, and lakehouse environments.