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Integrating the speed and versatility of Amazon EMR with the capabilities of Apache Hive offers an effective solution for managing big data. However, embarking on big data projects can be daunting. Whether you’re looking to deploy new data on EMR or transition an existing project, this guide will provide essential insights to facilitate the process.

Apache Hive is a widely-used open-source data warehousing and analytics tool that operates atop an Apache Hadoop cluster. The Hive metastore holds critical details about tables and their underlying data, encompassing partition names and data types. Hive is one of the applications that can be executed within EMR.

Most of the strategies discussed here assume that you are utilizing Apache Hadoop for managing your metastore, which enhances scalability for Hive. If you’re not using Hadoop, refer to the Amazon EMR documentation.

Hive Metastore Deployment

When migrating an on-premises Hadoop cluster to EMR, you have three configuration patterns to choose from for your Hive metastore: embedded, local, or remote. Your migration approach will depend on your existing metastore configuration.

It’s important to remember that Apache Hive comes bundled with the Derby database for embedded metastores; however, Derby is not suitable for production-level workloads due to its limited scalability.

In an EMR environment, Hive typically saves metastore information in a MySQL database on the master node’s ephemeral storage, resulting in a local metastore. When the cluster is terminated, all nodes, including the master node, shut down, leading to data loss.

To mitigate these issues, consider setting up an external Hive metastore. This ensures that your metadata can scale alongside your implementation and remains intact even if the cluster is decommissioned.

You have two primary options for establishing an external Hive metastore for EMR:

1. AWS Glue Data Catalog
2. Amazon RDS or Amazon Aurora

Utilizing the AWS Glue Data Catalog as the Hive Metastore

The AWS Glue Data Catalog is a flexible and reliable choice, particularly if you’re new to managing a metastore. Since AWS manages the service, you can invest less time and effort into maintenance, albeit with some loss of granular control. The Data Catalog is designed for high availability and fault tolerance, maintaining data replicas to prevent failure and scaling hardware based on usage.

You won’t need to separately manage the Hive metastore database instance, handle ongoing replication, or scale the instance manually. The AWS Glue Data Catalog can service one or multiple EMR clusters, and it also supports Amazon Athena and Amazon Redshift Spectrum. For additional reference, you can download the source code for the AWS Glue Data Catalog client for Apache Hive Metastore.

While the Data Catalog allows for significant control, it’s vital to note that it does not currently support column statistics, Hive authorizations, or Hive constraints.

The AWS Glue Data Catalog supports versioning, meaning tables can have multiple schema versions. AWS Glue retains that information, including the Hive metastore data. Depending on the catalog configuration, you can either adopt new schema versions or choose to ignore them.

When creating an EMR cluster with release version 5.8.0 or later, you can select the Data Catalog as your Hive metastore. This feature is not available in earlier versions.

Configuring the AWS Glue Data Catalog

To specify the AWS Glue Data Catalog during the EMR cluster setup, navigate to Advanced Options and enable Data Catalog settings in Step 1. Apache Hive, Presto, and Apache Spark can all utilize the Hive metastore within EMR.

Using Amazon RDS or Aurora as the Hive Metastore

If you desire complete control over your Hive metastore and wish to integrate with other open-source tools such as Apache Ranger or Apache Atlas, hosting your Hive metastore on Amazon RDS is a viable option.

Always remember that your Hive metastore represents a single point of failure. Amazon RDS does not replicate databases automatically, so it’s prudent to enable replication to prevent data loss in case of failure.

Setting up your Hive metastore using RDS or Aurora involves three main steps:

1. Create a MySQL or Aurora database.
2. Configure the hive-site.xml file to point to your MySQL or Aurora database.
3. Specify an external Hive metastore.

Conclusion

In summary, whether you choose to utilize the AWS Glue Data Catalog or Amazon RDS/Aurora for your Hive metastore, both options provide robust solutions for managing your data efficiently. For more insights on building a fulfilling career, check out this blog post. If you’re interested in benefits related to education, SHRM has valuable information on extending tax-free student loan repayment. Finally, for those preparing for interviews, this resource is an excellent guide.