Windowing Functions in Spark SQL Part 3 | Aggregation Functions | Windowing Functions Tutorial

Windowing Functions in Spark SQL Part 3 | Aggregation Functions | Windowing Functions Tutorial
Hello and welcome back to the series of windowing functions in Spark. In the previous video, you learned the internals of lag and lead functions. If you had missed it, please click the following video link for the better continuation.

In this Hadoop tutorial, you will be able to learn, how to perform aggregation with window functions using over clause.
To perform aggregations, we have pre-built functions like
• Min
• Max
• Count
• Average and Sum

To give you a brief idea of these aggregation functions, we will be using stock market data. You can download the sample stock data from the following links

The problem we are trying to solve here using this dataset is to get least closing value for all the tickers in the dataset.

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[sumitkumar-jpop]

Thanks for making this tutorial..really informative one!!

[abhiganta]

For cumulative running sum, order by date does not work, order by volume_of_the_day worked, what am I missing here?