Tag: EMR

Notebooks are increasingly becoming the standard tool for interactively developing big data applications. It’s easy to see why. Their flexible architecture allows you to experiment with data in multiple languages, test code interactively, and visualize large datasets. To help scientists and developers easily access notebook tools, we launched Amazon EMR Notebooks, a managed notebook environment […]

In this blog post, we introduce Deequ, an open source tool developed and used at Amazon. Deequ allows you to calculate data quality metrics on your dataset, define and verify data quality constraints, and be informed about changes in the data distribution. Instead of implementing checks and verification algorithms on your own, you can focus on describing how your data should look.

Many customers use Amazon EMR to run big data workloads, such as Apache Spark and Apache Hive queries, in their development environment. Data analysts and data scientists frequently use these types of clusters, known as analytics EMR clusters. Users often forget to terminate the clusters after their work is done. This leads to idle running […]

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 post was last reviewed and updated July, 2022 with a new bootstrap action script and log instructions. In a managed Apache Hadoop environment—like an Amazon EMR cluster—when the storage capacity on your cluster fills up, there is no convenient solution to deal with it. This situation occurs because you set up Amazon Elastic Block […]

The AWS Big Data Blog has a large community of authors who are passionate about Apache Spark and who regularly publish content that helps customers use Spark to build real-world solutions. You’ll see content on a variety of topics, including deep-dives on Spark’s internals, building Spark Streaming applications, creating machine learning pipelines using MLlib, and ways […]