While we’re waiting on the cluster to start, let’s take a moment to cover our setup in a bit more detail. That’s it! After about ten minutes, you’ll have everything you need to start reading and writing data between your EMR compute engines and your data warehouse powered by Iceberg. Just click “Next” through the rest of the sections, and at the end, click “Create Cluster.” I’ve chosen “Iceberg on EMR.”įor the rest of the options, you can leave the defaults. In the “General Cluster Settings” section, provide a name for the cluster. ".dynamodb.table-name": "prodiceberg_metastore" } ".catalog-impl": ".dynamodb.DynamoDbCatalog", "": ".extensions.IcebergSparkSessionExtensions", with a path to the location on S3 that you want to use for your warehouse, e.g.
#Install apache spark on ec2 ubuntu software
In the “Edit software settings” section, enter the following configuration into the text box. Next, check the box for all of the following applications First, switch the EMR version to 6.4.0 in the drop-down selector. To create the EMR cluster, use the advanced create cluster setup menu found here.ĭon’t worry we’ll only have to set a couple of things here. Professionals to effectively manage big data, even up to tens of petabytes in size. Along with high-performance queries on data at rest, it comes with a ton of features that enable data
That can integrate with an ever-growing number of compute engines.
If you’re unfamiliar with Iceberg, it’s a table format for analytic datasets In this post, I’ll walk you through creating an EMR cluster backed by Apache Iceberg tables. If you’re not familiar with EMR, it’s a simple way to get a Spark cluster running in about ten minutes. Many AWS customers already use EMR to run their Spark clusters.