Category: Amazon EMR

Since the deluge of big data over a decade ago, many organizations have learned to build applications to process and analyze petabytes of data. Data lakes have served as a central repository to store structured and unstructured data at any scale and in various formats. However, as data processing at scale solutions grow, organizations need […]

Building data lakes from continuously changing transactional data of databases and keeping data lakes up to date is a complex task and can be an operational challenge. A solution to this problem is to use AWS Database Migration Service (AWS DMS) for migrating historical and real-time transactional data into the data lake. You can then […]

Apache Iceberg is an open table format for very large analytic datasets, which captures metadata information on the state of datasets as they evolve and change over time. It adds tables to compute engines including Spark, Trino, PrestoDB, Flink, and Hive using a high-performance table format that works just like a SQL table. Iceberg has […]

In June 2020, AWS announced the general availability of Amazon EMR Managed Scaling. With EMR Managed Scaling, you specify the minimum and maximum compute limits for your clusters, and Amazon EMR automatically resizes your cluster for optimal performance and resource utilization. EMR Managed Scaling constantly monitors key workload-related metrics and uses an algorithm that optimizes the […]

This is a guest post by Nan Zhu, Tech Lead Manager, SafeGraph, and Dave Thibault, Sr. Solutions Architect – AWS SafeGraph is a geospatial data company that curates over 41 million global points of interest (POIs) with detailed attributes, such as brand affiliation, advanced category tagging, and open hours, as well as how people interact […]

Amazon EMR Serverless is a serverless option in Amazon EMR that makes it simple to run applications using open-source analytics frameworks such as Apache Spark and Hive without configuring, managing, or scaling clusters. At AWS re:Invent 2022, we announced support for running serverless Spark and Hive workloads with AWS Graviton2 (Arm64) on Amazon EMR Serverless. […]

Amazon EMR Serverless allows you to run open-source big data frameworks such as Apache Spark and Apache Hive without managing clusters and servers. With EMR Serverless, you can run analytics workloads at any scale with automatic scaling that resizes resources in seconds to meet changing data volumes and processing requirements. EMR Serverless automatically scales resources up […]

Amazon EMR provides a managed Apache Hadoop framework that makes it straightforward, fast, and cost-effective to run Apache HBase. Apache HBase is a massively scalable, distributed big data store in the Apache Hadoop ecosystem. It is an open-source, non-relational, versioned database that runs on top of the Apache Hadoop Distributed File System (HDFS). It’s built […]

In the post Introducing the AWS ProServe Hadoop Migration Delivery Kit TCO tool, we introduced the AWS ProServe Hadoop Migration Delivery Kit (HMDK) TCO tool and the benefits of migrating on-premises Hadoop workloads to Amazon EMR. In this post, we dive deep into the tool, walking through all steps from log ingestion, transformation, visualization, and […]

When migrating Hadoop workloads to Amazon EMR, it’s often difficult to identify the optimal cluster configuration without analyzing existing workloads by hand. To solve this, we’re introducing the Hadoop migration assessment Total Cost of Ownership (TCO) tool. You now have a Hadoop migration assessment TCO tool within the AWS ProServe Hadoop Migration Delivery Kit (HMDK). […]