Tag: HAQM EMR

NOTE: As of November 2018, you can run Apache Flink programs with HAQM Kinesis Analytics for Java Applications in a fully managed environment. You can find further details in a new blog post on the AWS Big Data Blog and in this Github repository. ————————– September 8, 2021: HAQM Elasticsearch Service has been renamed to HAQM OpenSearch Service. See details. […]

Sometimes, data to be analyzed is spread across buckets owned by different accounts. In order to ensure data security, appropriate credentials management needs to be in place. This is especially true for large enterprises storing data in different HAQM S3 buckets for different departments. For example, a customer service department may need access to data […]

Want to change the world with Big Data and Analytics? Come join us on the HAQM EMR team in HAQM Web Services! Meet the HAQM EMR team this Friday April 7th from 5:00 – 7:30 PM at Michael’s at Shoreline in Mountain View. We’ll feature short tech talks by EMR leadership who will talk about the past, […]

September 8, 2021: HAQM Elasticsearch Service has been renamed to HAQM OpenSearch Service. See details. You have come up with an exciting hypothesis, and now you are keen to find and analyze as much data as possible to prove (or refute) it. There are many datasets that might be applicable, but they have been created […]

In the last few years, there has been a rapid rise in enterprises adopting the Apache Hadoop ecosystem for critical workloads that process sensitive or highly confidential data. Due to the highly critical nature of the workloads, the enterprises implement certain organization/industry wide policies and certain regulatory or compliance policies. Such policy requirements are designed […]

As anyone visiting their doctor may have noticed, gone are the days of physicians recording their notes on paper. Physicians are more likely to enter the exam room with a laptop than with paper and pen. This change is the byproduct of efforts to improve patient outcomes, increase efficiency, and drive population health. Pushing for […]

The typical progression for creating and using a trained model for recommendations falls into two general areas: training the model and hosting the model. Model training has become a well-known standard practice. We want to highlight one of many ways to host those recommendations (for example, see the Analyzing Genomics Data at Scale using R, […]

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 HAQM EMR console. We will not provide any […]

Jonathan Fritz is a Senior Product Manager for HAQM EMR Customers can take advantage of the HAQM 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 HAQM EMR provides a managed Hadoop framework that makes it easy, fast, and cost-effective to process vast amounts of data across dynamically scalable HAQM EC2 instances. HAQM EMR defines three types of nodes: master node, core nodes, and task nodes. It’s common to […]