Spark SQL is the structured data processing module within Apache Spark, combining the power of SQL queries with DataFrame/Dataset APIs for large-scale distributed data analysis. It operates on DataFrames—strongly-typed, distributed collections resembling database tables—and leverages the Catalyst optimizer to generate efficient execution plans automatically. Understanding how Spark SQL translates high-level operations into optimized physical execution, manages data partitioning across clusters, and chooses join strategies is essential for building scalable data pipelines that process terabytes efficiently without manual tuning.