In data analytics, efficiently aggregating large datasets is a basic need. For example, when working with retail inventory data, tracking products shipped to stores each month, the standard GROUP BY clause in SQL can handle basic aggregations.
However, it falls short when you need multiple levels of aggregation in a single query. This is where Spark SQL’s advanced GROUP BY extensions, GROUPING SETS, ROLLUP, and CUBE, come into the picture to compute multiple groupings efficiently.