How You Should Save the Output of your Spark ETL Jobs (If you are not Writing to a Database)
Last Updated on July 20, 2023 by Editorial Team
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Originally published on Towards AI.
In this article, I will share my thoughts on the best way to save the output of Spark ETL jobs so that it is easier to do analytical work later. The code to reproduce the examples can be found here.
The cluster I used to run the code in this article is hosted on Databricks with the following configuration:
Cluster Mode: StandardDatabricks Runtime Version: 5.5 LTS ML (includes Apache Spark 2.4.3 Scala 2.11)
There are 8 workers and both the workers and driver are m4.xlarge instances (16.0 GB, 4 Cores).
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