Category: Amazon EMR

November 2024: This post was reviewed and updated for accuracy. The EMRFS S3-optimized committer is a new output committer available for use with Apache Spark jobs as of Amazon EMR 5.19.0. This committer improves performance when writing Apache Parquet files to Amazon S3 using the EMR File System (EMRFS). In this post, we run a performance […]

This blog post provides an overview of the issues with how open-source Spark handles node loss and the improvements in Amazon EMR to address the issues.

This blog post was last reviewed and updated April, 2022. The code repositories used in this blog have been reviewed and updated to fix the solution With the ever-evolving and growing role of data in today’s world, data governance is an essential aspect of effective data management. Many organizations use a data lake as a […]

Apache Spark and Hadoop are popular frameworks to process data for analytics, often at a fraction of the cost of legacy approaches, yet at scale they may still become expensive propositions. This blog post discusses ways to reduce your total costs of ownership, while also improving staff productivity at the same time. This can be […]

This post walks you through some of the principles of Amazon EMR security. It also describes features that you can use in Amazon EMR to help you meet the security and compliance objectives for your business. We cover some common security best practices that we see used. We also show some sample configurations to get you started.

In this blog post, we’ll go through the steps needed to build an ETL pipeline that consumes from one source in one VPC and outputs it to another source in a different VPC. We’ll set up in multiple VPCs to reproduce a situation where your database instances are in multiple VPCs for isolation related to security, audit, or other purposes.

February 2025: The bootstrap action script in this blog post uses IMDS v1 for accessing EC2 instance metadata. The script does not support IMDS v2 and cannot be used in an AWS account which has IMDS v2 enforced across the account. Using the script in an IMDS v2 enabled account will cause issues and unexpected […]

This whitepaper walks you through the stages of a migration. It also helps you determine when to choose Apache HBase on Amazon S3 on Amazon EMR, plan for platform security, tune Apache HBase and EMRFS to support your application SLA, identify options to migrate and restore your data, and manage your cluster in production.

In this blog post, we discuss how to build a real-time IoT stream processing, visualization, and alerting pipeline using various AWS services. We took advantage of the Complex Event Processing feature provided by Apache Flink to detect patterns within a network from the incoming events.

This blog post shows how our customers can benefit by using the Apache Sqoop tool. This tool is designed to transfer and import data from a Relational Database Management System (RDBMS) into AWS – EMR Hadoop Distributed File System (HDFS), transform the data in Hadoop, and then export the data into a Data Warehouse (e.g. in Hive or Amazon Redshift).