This section describes common use cases when you work with EMR Serverless applications. This includes a variety of tools including Hudi and Iceberg for working on large data sets and using Python and Python libraries to submit Spark jobs.
Using Java 17 with Amazon EMR Serverless
Using Apache Hudi with EMR Serverless
Using Apache Iceberg with EMR Serverless
Using Python libraries with EMR Serverless
Using different Python versions with EMR Serverless
Using Delta Lake OSS with EMR Serverless
Submitting EMR Serverless jobs from Airflow
Using Hive user-defined functions with EMR Serverless
Using custom images with EMR Serverless
Using Amazon Redshift integration for Apache Spark on Amazon EMR Serverless
Connecting to DynamoDB with Amazon EMR Serverless
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