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Calculate Standard deviation incrementally one batch at a time

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In this article, I would like to display a way to calculate standard deviation in an incremental way. Unlike existing articles, that shows how to update the calculation when adding one more element. I will display a method to calculate standard deviation (std.dev) from existing batch of data and additional batch of data. Motivation - suppose you have an ETL system that processes batches of data every day like Apache Spark and you need to display statistical data for all the data like std.dev. If you calculate it on the entire set of data the calculation will become increasingly "harder" since the data set will always grow in size and the calculation will require more and more time. The definition of standard deviation is: So once additional data is added the entire formula should be re-calculated. And the "hard" parts (where we do a full scan of the data) is in the average and the squared difference from the mean should be recalculated. How...
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Create Custom Datasource With Spark 3 - Part 1

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This article is the first of a series on how to create a custom data source that will plug in to Spark mechanism. The idea is to write something like: Dataset < Row > df = sparkSession . read () . option ( "filename" , "path-to-data-file" ) . option ( "other-options" , "some value" ) . format ( "com.roizaig.mydatasource" ) . load (); To read a file, parse it according to some custom logic and return a dataset that represents it. Why Create Custom Data Source? Spark support out of the box in Parquet , CSV , JSON and ORC  and more. In most cases these formats will be more than okay and if not, you can always read it as a text file and do some string manipulation in Spark. However, there are some cases where we would like to "teach" Spark to read our own custom format. Flexibility is a key point, with a custom datasource you can read every format y...
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Spark - How to read a JSON file

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