PySpark Aggregations Explained | Group By, Having, Collect Set, and Window Functions in Databricks

Описание: In this video, we will explore all types of aggregation operations in PySpark — from basic to advanced. You’ll learn how to use functions like count, min, max, sum, avg, and then move to group by, having, and window functions such as row_number, rank, and dense_rank.

What you’ll learn:
Basic aggregations: count, min, max, sum, avg
Using groupBy() and having clause in PySpark and SQL
Difference between collect_set and collect_list
How to perform advanced aggregations with window functions
Understanding row_number, rank, and dense_rank differences
Real-world use cases of window functions
Equivalent SQL syntax for all aggregations

This tutorial is perfect for anyone learning PySpark aggregation concepts or preparing for Spark interviews.

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