Author: Suthan Phillips

In this post, we show you how the read-replica prewarm feature of Amazon EMR 7.12 improves HBase cluster operations by minimizing the hard cutover constraints that make infrastructure changes challenging. This feature gives you a consistent blue-green deployment pattern that reduces risk and downtime for version upgrades and security patches.

In this post, we dive deep into the new Amazon EMR WAL feature to help you understand how it works, how it enhances durability, and why it’s needed. We explore several scenarios that are well-suited for this feature.

Apache Hudi is an open-source transactional data lake framework that greatly simplifies incremental data processing and data pipeline development. It does this by bringing core warehouse and database functionality directly to a data lake on Amazon Simple Storage Service (Amazon S3) or Apache HDFS. Hudi provides table management, instantaneous views, efficient upserts/deletes, advanced indexes, streaming […]

Our customers use Apache Hive on Amazon EMR for large-scale data analytics and extract, transform, and load (ETL) jobs. Amazon EMR Hive uses Apache Tez as the default job execution engine, which creates Directed Acyclic Graphs (DAGs) to process data. Each DAG can contain multiple vertices from which tasks are created to run the application […]

December 2022: The best practice of using EMRFS consistent in this blog post is now obsolete as Amazon S3 has supported strong read-after-write consistency since December, 2020.  Apache Hive is an open-source data warehouse package that runs on top of an Apache Hadoop cluster. You can use Hive for batch processing and large-scale data analysis. […]

Customers use Apache Hive with Amazon EMR to provide SQL-based access to petabytes of data stored on Amazon S3. Amazon EMR 6.0.0 adds support for Hive LLAP, providing an average performance speedup of 2x over EMR 5.29, with up to 10x improvement on individual Hive TPC-DS queries. This post shows you how to enable Hive […]