Category: Amazon EMR

Amazon EMR now supports M6g, C6g and R6g instances with Amazon EMR versions 6.1.0, 5.31.0 and later. These instances are powered by AWS Graviton2 processors that are custom designed by AWS using 64-bit Arm Neoverse cores to deliver the best price performance for cloud workloads running in Amazon Elastic Compute Cloud (Amazon EC2). On Graviton2 […]

The machine learning (ML) lifecycle consists of several key phases: data collection, data preparation, feature engineering, model training, model evaluation, and model deployment. The data preparation and feature engineering phases ensure an ML model is given high-quality data that is relevant to the model’s purpose. Because most raw datasets require multiple cleaning steps (such as […]

Many organizations have an Apache Hive metastore that stores the schemas for their data lake. You can use Amazon Athena due to its serverless nature; Athena makes it easy for anyone with SQL skills to quickly analyze large-scale datasets. You may also want to reliably query the rich datasets in the lake, with their schemas […]

Amazon EMR is a big data service offered by AWS to run Apache Spark and other open-source applications on AWS in a cost-effective manner. Amazon EMR Notebooks is a managed environment based on Jupyter Notebook that allows data scientists, analysts, and developers to prepare and visualize data, collaborate with peers, build applications, and perform interactive […]

This post is co-written by Parijat Sharma: Principal, Strategy & Transformation, Wenhao Xia: Manager, Data Science, Vineeth Sandadi: Manager, Business Consulting from ZS Associates, Inc, Arianna Tousi: Strategy, Insights and Planning Consultant from ZS, Gopi Vikranth: Associate Principal from ZS. In their own words, “We’re passionately committed to helping our clients and their customers thrive, […]

Amazon EMR now supports the capacity-optimized allocation strategy for Amazon Elastic Compute Cloud (Amazon EC2) Spot Instances for launching Spot Instances from the most available Spot Instance capacity pools by analyzing capacity metrics in real time. You can now specify up to 15 instance types in your EMR task instance fleet configuration. This provides Amazon […]

Data lakes give organizations the ability to harness data from multiple sources in less time. Users across different roles are now empowered to collaborate and analyze data in different ways, leading to better, faster decision-making. Amazon Simple Storage Service (Amazon S3) is the highly performant object storage service for structured and unstructured data and the […]

Amazon EMR allows you to process vast amounts of data quickly and cost-effectively at scale. Using open-source tools such as Apache Spark, Apache Hive, and Presto, and coupled with the scalable storage of Amazon Simple Storage Service (Amazon S3), Amazon EMR gives analytical teams the engines and elasticity to run petabyte-scale analysis for a fraction […]

As Amazon EMR continues its widespread adoption, it’s important to enforce separation of duties using role-based access when submitting your hive jobs on EMR clusters in multi-tenant environments. In this post, we walk through the steps to set up authentication for Hive using Lightweight Directory Access Protocol (LDAP) and Microsoft Active Directory Domain Controller. Solution […]

December 2022: The best practice of using EMRFS consistent in this blog post is now obsolete as Amazon S3 has supported strong read-after-write consistency since December, 2020.  Apache Hive is an open-source data warehouse package that runs on top of an Apache Hadoop cluster. You can use Hive for batch processing and large-scale data analysis. […]