Tag: Amazon EMR

NOTE: Please note that as of EMR 5.14.0, JupyterHub is an officially supported application. We recommend you use the most recent version of EMR if you would like to run JupyterHub on EMR. In addition, EMR Notebooks allow you to create and open Jupyter notebooks with the Amazon EMR console. We will not provide any […]

Jonathan Fritz is a Senior Product Manager for Amazon EMR Customers can take advantage of the Amazon EMR API to create and terminate EMR clusters, scale clusters using Auto Scaling or manual resizing, and submit and run Apache Spark, Apache Hive, or Apache Pig workloads. These decisions are often triggered from cluster state-related information. Previously, […]

Miguel Tormo is a Big Data Support Engineer in AWS Premium Support Amazon EMR provides a managed Hadoop framework that makes it easy, fast, and cost-effective to process vast amounts of data across dynamically scalable Amazon EC2 instances. Amazon EMR defines three types of nodes: master node, core nodes, and task nodes. It’s common to […]

Updated 3/30/2022: Amazon EMR has announced official support of Apache Ranger (link). Open-source plugin support will not be maintained moving forward and compatibility with latest versions will not be tested. We recommend customers to move to the Amazon EMR support for Apache Ranger. Ranger Presto plugin support on EMR has been deprecated. Updated 12/03/2020: Support for […]

John Hitchingham is Director of Performance Engineering at FINRA The Financial Industry Regulatory Authority (FINRA) is a private sector regulator responsible for analyzing 99% of the equities and 65% of the option activity in the US. In order to look for fraud, market manipulation, insider trading, and abuse, FINRA’s technology group has developed a robust […]

Jonathan Fritz is a Senior Product Manager for Amazon EMR Customers running Apache Spark, Presto, and the Apache Hadoop ecosystem take advantage of Amazon EMR’s elasticity to save costs by terminating clusters after workflows are complete and resizing clusters with low-cost Amazon EC2 Spot Instances. For instance, customers can create clusters for daily ETL or machine learning […]

Today we are making it even easier to run Flink on AWS as it is now natively supported in Amazon EMR 5.1.0. EMR supports running Flink-on-YARN so you can create either a long-running cluster that accepts multiple jobs or a short-running Flink session in a transient cluster that helps reduce your costs by only charging you for the time that you use.

This post was last updated July 7th, 2021 (original version by Tom Zeng). The Sparklyr package by RStudio has made processing big data in R a lot easier. Sparklyr is an R interface to Spark, it allows using Spark as the backend for dplyr – one of the most popular data manipulation packages. Sparklyr also […]

In this post, I discuss some of the practical challenges faced during the implementation of the data lake for Eliza and the corresponding details of the ways we solved these issues with AWS. The challenges we faced involved the variety of data and a need for a common view of the data.

In this post, I walk you through an architectural approach as well as a sample implementation on how to collect, process, and analyze data for event-driven applications in AWS.