Transforming Data with Spark SQL

Create and use temporary views to modularize transformation logic within a Spark session.

Apply common table expressions (CTEs) to define sequential steps in a multi-stage ELT query.

Use CTAS (CREATE TABLE AS SELECT) to create Delta tables from query results.

Apply Spark SQL functions for string manipulation (e.g., split, concat, length).

Apply Spark SQL functions for date and timestamp handling (e.g., to_date, date_format).

Use CAST to perform data type conversions in Spark SQL.

Reshape data using PIVOT and UNPIVOT operations.

Parse and extract fields from nested JSON data and flatten arrays using EXPLODE.