Spark SQL Explained: Architecture, Catalyst Optimizer, and Key Differences from Traditional Databases
Apache Spark SQL bridges the gap between traditional SQL processing and big data analytics. Built on a distributed architecture, Spark SQL combines the familiarity of SQL syntax with the scalability of Spark’s in-memory engine. This article explores how Spark SQL works, its Catalyst Optimizer and Tungsten execution engine, and how it differs from conventional RDBMS systems. You’ll also learn about its integration with machine learning, unified data access, and use in large-scale ETL and analytics workflows.
