Data Engineering Spark SQL - Windowing Functions - Aggregations using Windowing Functions

Let us see how we can perform aggregations within a partition or group using Windowing/Analytics Functions. Let us start spark context for this Notebook so that we can execute the code provided.

Explanation for the video

  • The video provided below demonstrates how to use Windowing/Analytics Functions to perform aggregations within partitions or groups in Spark SQL.

Click here to watch the video

Key Concepts Explanation

The key concept here is using Windowing Functions to perform aggregations within a partition or group in Spark SQL.

Using Windowing Functions

Windowing functions allow us to compute aggregations within a partition or group specified by the PARTITION BY clause. Here’s an example code snippet:

SELECT e.employee_id, e.department_id, e.salary,
sum(e.salary) OVER (PARTITION BY e.department_id) AS department_salary_expense
FROM employees e
ORDER BY e.department_id

Create tables to get daily revenue

This part of the code demonstrates how to create tables to compute daily revenue and daily product revenue using Windowing/Analytics Functions.

Hands-On Tasks

Here are some hands-on tasks that you can perform to apply the concepts discussed in the article:

  1. Start your Spark SQL session using Spark Shell or Pyspark
  2. Execute the provided example code snippets to understand how Windowing Functions work

Conclusion

In this article, we covered how to use Windowing/Analytics Functions to perform aggregations within partitions or groups in Spark SQL. We also created tables to compute daily revenue and daily product revenue. I encourage you to practice these concepts and engage with the community for further learning.

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Watch the video tutorial here