AWS Big Data Blog

Category: Amazon EMR

Implementing Authorization and Auditing using Apache Ranger on Amazon EMR

Updated 9/26/2018: Updates have been made to support the latest versions of EMR and Apache Ranger. ————————————————– Role-based access control (RBAC) is an important security requirement for multi-tenant Hadoop clusters. Enforcing this across always-on and transient clusters can be hard to set up and maintain. Imagine an organization that has an RBAC matrix using Active […]

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Low-Latency Access on Trillions of Records: FINRA’s Architecture Using Apache HBase on Amazon EMR with Amazon S3

John Hitchingham is Director of Performance Engineering at FINRA The Financial Industry Regulatory Authority (FINRA) is a private sector regulator responsible for analyzing 99% of the equities and 65% of the option activity in the US. In order to look for fraud, market manipulation, insider trading, and abuse, FINRA’s technology group has developed a robust […]

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Dynamically Scale Applications on Amazon EMR with Auto Scaling

Jonathan Fritz is a Senior Product Manager for Amazon EMR Customers running Apache Spark, Presto, and the Apache Hadoop ecosystem take advantage of Amazon EMR’s elasticity to save costs by terminating clusters after workflows are complete and resizing clusters with low-cost Amazon EC2 Spot Instances. For instance, customers can create clusters for daily ETL or machine learning […]

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Use Apache Flink on Amazon EMR

Today we are making it even easier to run Flink on AWS as it is now natively supported in Amazon EMR 5.1.0. EMR supports running Flink-on-YARN so you can create either a long-running cluster that accepts multiple jobs or a short-running Flink session in a transient cluster that helps reduce your costs by only charging you for the time that you use.

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Encrypt Data At-Rest and In-Flight on Amazon EMR with Security Configurations

ustomers running analytics, stream processing, machine learning, and ETL workloads on personally identifiable information, health information, and financial data have strict requirements for encryption of data at-rest and in-transit. The Apache Spark and Hadoop ecosystems lend themselves to these big data use cases, and customers have asked us to provide a quick and easy way to encrypt data at-rest and data in-transit between nodes in each execution framework.

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