# Using Apache Hudi with EMR Serverless
<a name="using-hudi"></a>

This section describes using Apache Hudi with EMR Serverless applications. Hudi is a data-management framework that makes data processing more simple.

**To use Apache Hudi with EMR Serverless applications**

1. Set the required Spark properties in the corresponding Spark job run.

   ```
   spark.jars=/usr/lib/hudi/hudi-spark-bundle.jar,/usr/lib/hudi/hudi-utilities-bundle.jar,/usr/lib/hudi/hudi-aws-bundle.jar
   spark.serializer=org.apache.spark.serializer.KryoSerializer
   ```

1. To sync a Hudi table to the configured catalog, designate either the Amazon Glue Data Catalog as your metastore, or configure an external metastore. EMR Serverless supports `hms` as the sync mode for Hive tables for Hudi workloads. EMR Serverless activates this property as a default. To learn more about how to set up your metastore, refer to [Metastore configuration for EMR Serverless](metastore-config.md). 
**Important**  
EMR Serverless doesn't support `HIVEQL` or `JDBC` as sync mode options for Hive tables to handle Hudi workloads. To learn more, refer to [Sync modes](https://hudi.apache.org/docs/next/syncing_metastore/#sync-modes).

   When you use the Amazon Glue Data Catalog as your metastore, specify the following configuration properties for your Hudi job.

   ```
   --conf spark.jars=/usr/lib/hudi/hudi-spark-bundle.jar,
   --conf spark.serializer=org.apache.spark.serializer.KryoSerializer,
   --conf spark.hadoop.hive.metastore.client.factory.class=com.amazonaws.glue.catalog.metastore.AWSGlueDataCatalogHiveClientFactory
   ```

To learn more about Apache Hudi releases of Amazon EMR, refer to [Hudi release history](https://docs.amazonaws.cn/emr/latest/ReleaseGuide/Hudi-release-history.html).