Spark provides a powerful MODULE called SparkSQL which performs relational data processing combined with the power of the functional PROGRAMMING feature of Spark. This module also supports either by means of SQL or HIVE Query Language. It also provides support for different data sources and helps developers write powerful SQL queries using code transformations.
The four major libraries of SparkSQL are:

• Data Source API
• DataFrame API
• Interpreter & Catalyst Optimizer
• SQL Services

Spark SQL supports the usage of structured and semi-structured data in the following ways:

• Spark supports DataFrame abstraction in various languages like Python, Scala, and Java along with providing good optimization techniques.
• SparkSQL supports data read and writes operations in various structured formats like JSON, Hive, Parquet, etc.
• SparkSQL allows data querying inside the Spark PROGRAM and VIA external tools that do the JDBC/ODBC connections.
• It is recommended to use SparkSQL inside the Spark applications as it empowers the developers to load the data, query the data from databases and write the results to the destination.