SPARK Upgrade on EMR best practices

Question

Hello Team - Good Morning. My customer is using SPARK 2.4 on EMR for their batch workloads. They are planning for migration to SPARK 3.3 and looking for some guidance/best practices for this migration. Can you please share if any reference docs/blog posts etc.. related to this topic. Thanks in advance

Answer

You can use Spark section from this [EMR best practices guide](https://aws.github.io/aws-emr-best-practices/applications/spark/best_practices/). 
Feel free to share here or create a specReq if customer has any specific question. Here are few basic things to keep in mind.

* Handle data skew
* Make sure there is no disk spill happening
* Optimal partition size to make sure not too many tasks are created
*  Use the right data format for source and target (preferably parquet)
*  Watch for excessive shuffle. Can be confirmed from Spark UI.
*  Tune driver/executor size (memory, core) based on workload.

SPARK Upgrade on EMR best practices

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