Pivot was first introduced in Apache Spark 1.6 as a new DataFrame feature that allows users to rotate a table-valued expression by turning the unique values from one column into individual columns.
The upcoming Apache Spark 2.4 release extends this powerful functionality of pivoting data to our SQL users as well. In this blog, using temperatures recordings in Seattle, we’ll show how we can use this common SQL Pivot feature to achieve complex data transformations.
The syntax is quite similar to the
PIVOT syntax that SQL Server uses.