Category: Amazon EMR

This post explores how you can use AWS Lake Formation integration with Amazon EMR (still in beta) to implement fine-grained column-level access controls while using Spark in a Zeppelin Notebook. My previous post Extract Salesforce.com data using AWS Glue and analyzing with Amazon Athena showed you a simple use case for extracting any Salesforce object data using AWS Glue and Apache Spark, saving it to Amazon Simple Storage Service (Amazon S3), cataloging the data using the Data Catalog in Glue, and querying it using Amazon Athena.

This is a guest post by Kong Zhao, Solution Architect at NVIDIA Corporation This post shares how NVIDIA sped up RAPIDS XGBoost performance up to 4.5 times faster and reduced costs up to 5.4 times less by using Amazon EMR running Amazon Elastic Compute Cloud (Amazon EC2) G4 instances. Gradient boosting is a powerful machine […]

What if you could control the access to your data lake centrally? Would it be more convenient to share specific data securely with internal and external customers? With AWS Lake Formation and its integration with Amazon EMR, you can easily perform these administrative tasks. This post goes through a use case and reviews the steps to control the data access and permissions of your existing data lake.

AWS is happy to announce the release of Amazon EMR Managed Scaling—a new feature that automatically resizes your cluster for best performance at the lowest possible cost. With EMR Managed Scaling you specify the minimum and maximum compute limits for your clusters and Amazon EMR automatically resizes them for best performance and resource utilization. EMR Managed Scaling continuously samples key metrics associated with the workloads running on clusters. EMR Managed Scaling is supported for Apache Spark, Apache Hive and YARN-based workloads on Amazon EMR versions 5.30.1 and above.

Amazon EMR web interfaces are hosted on the master node of an EMR cluster. When you launch an EMR cluster in a private subnet, the EMR master node doesn’t have a public DNS record. The web interfaces hosted in a private subnet aren’t easily accessible outside the subnet. You can use an Application Load Balancer (ALB), launched in a public subnet, as an HTTPS proxy to access EMR web interfaces over the internet without requiring SSH tunneling through a bastion host. This approach greatly simplifies accessing EMR web interfaces. This post outlines how to use an ALB to securely access EMR web interfaces over the internet for an EMR cluster launched in a private subnet.

This post demonstrates how to implement a simple SparkListener, monitor and observe Spark streaming applications, and set up some alerts. The post also shows how to use alerts to set up automatic scaling on Amazon EMR clusters, based on your CloudWatch custom metrics.

This post details how we designed and implemented our data lake’s batch ETL pipeline to use Amazon EMR, and the numerous ways we iterated on its architecture to reduce Apache Spark runtimes from hours to minutes and save over 50% on operational costs.

Building a modern data platform on AWS enables you to collect data of all types, store it in a central, secure repository, and analyze it with purpose-built tools. Yet you may be unsure of how to get started and the impact of certain design decisions. To address the need to provide advice tailored to specific technology and application domains, AWS added the concept of well-architected lenses 2017. AWS now is happy to announce the Analytics Lens for the AWS Well-Architected Framework. This post provides an introduction of its purpose, topics covered, common scenarios, and services included.

This post demonstrates how to extend the metadata contained in the Data Catalog with profiling information calculated with an Apache Spark application based on the Amazon Deequ library running on an EMR cluster. You can query the Data Catalog using the AWS CLI. You can also build a reporting system with Athena and Amazon QuickSight to query and visualize the data stored in Amazon S3.

RStudio is an integrated development environment (IDE) for R, a language and environment for statistical computing and graphics. As a data scientist, you may integrate R and Spark (a big data processing framework) to analyze large datasets. You can use an R package called sparklyr to offload filtering and aggregation of large datasets from your […]