Amazon EMR

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

Benefits

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.

Use Cases

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.
Extract data from a variety of sources, process it at scale, and make it available for applications and users.
Analyze events from streaming data sources in real-time to create long-running, highly available, and fault-tolerant streaming data pipelines.
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.

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