Apache Spark: Difference between Spark Core and Spark SQL

Spark Core is the foundation of Apache Spark and provides the basic functionality for distributed data processing, including resilient distributed datasets (RDDs) and transformations. Spark SQL, on the other hand, is a component of Apache Spark that enables users to run SQL queries on Spark data structures, including RDDs and DataFrames.

The main difference between Spark Core and Spark SQL is that Spark Core focuses on low-level APIs for distributed data processing, while Spark SQL provides a higher-level interface for working with structured data using SQL queries. Spark SQL also offers optimized execution of SQL queries through the Catalyst query optimizer and supports integration with external data sources.

Overall, understanding the differences between Spark Core and Spark SQL is essential for developing efficient and scalable data processing pipelines in Apache Spark.