Amazon EMR

Easily run and scale Apache Spark, Hive, Presto, and other big data workloads

Run big data applications and petabyte-scale data analytics faster, and at less than half the cost of on-premises solutions.

Build applications using the latest open-source frameworks, with options to run on customized Amazon EC2 clusters, Amazon EKS, AWS Outposts, or Amazon EMR Serverless.

Get up to 2X faster time-to-insights with performance-optimized and open-source API-compatible versions of Spark, Hive, and Presto.

Easily develop, visualize, and debug your applications using EMR Notebooks and familiar open-source tools in EMR Studio.

How it works

Amazon EMR is a cloud big data platform for running large-scale distributed data processing jobs, interactive SQL queries, and machine learning (ML) applications using open-source analytics frameworks such as Apache Spark, Apache Hive, and Presto.

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Introducing EMR Serverless (2:02)

Introducing Amazon EMR Serverless

Amazon EMR Serverless is a new option in Amazon EMR that makes it easy and cost-effective for data engineers and analysts to run applications built using open source big data frameworks such as Apache Spark, Hive or Presto, without having to tune, operate, optimize, secure or manage clusters. EMR Serverless scales compute and memory resources up or down as needed by your application and d you only pay for resources used by your application.

Use cases

Perform big data analytics

Run large-scale data processing and what-if analysis using statistical algorithms and predictive models to uncover hidden patterns, correlations, market trends, and customer preferences.

Build scalable data pipelines

Extract data from a variety of sources, process it at scale, and make it available for applications and users.

Process real-time data streams

Analyze events from streaming data sources in real-time to create long-running, highly available, and fault-tolerant streaming data pipelines.

Accelerate data science and ML adoption

Analyze data using open-source ML frameworks such as Apache Spark MLlib, TensorFlow, and Apache MXNet. Connect to Amazon SageMaker Studio for large-scale model training, analysis, and reporting.