AWS Big Data Blog

Category: Amazon EMR

Harmonize, Search, and Analyze Loosely Coupled Datasets on AWS

You have come up with an exciting hypothesis, and now you are keen to find and analyze as much data as possible to prove (or refute) it. There are many datasets that might be applicable, but they have been created at different times by different people and don’t conform to any common standard. They use […]

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Secure Amazon EMR with Encryption

In the last few years, there has been a rapid rise in enterprises adopting the Apache Hadoop ecosystem for critical workloads that process sensitive or highly confidential data. Due to the highly critical nature of the workloads, the enterprises implement certain organization/industry wide policies and certain regulatory or compliance policies. Such policy requirements are designed […]

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Serving Real-Time Machine Learning Predictions on Amazon EMR

The typical progression for creating and using a trained model for recommendations falls into two general areas: training the model and hosting the model. Model training has become a well-known standard practice. We want to highlight one of many ways to host those recommendations (for example, see the Analyzing Genomics Data at Scale using R, […]

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Run Jupyter Notebook and JupyterHub on Amazon EMR

NOTE: The content in this post may need periodic updates as newer versions become available. Please leave a comment if you have any trouble implementing this solution. Tom Zeng is a Solutions Architect for Amazon EMR Jupyter Notebook (formerly IPython) is one of the most popular user interfaces for running Python, R, Julia, Scala, and […]

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Respond to State Changes on Amazon EMR Clusters with Amazon CloudWatch Events

Jonathan Fritz is a Senior Product Manager for Amazon EMR Customers can take advantage of the Amazon EMR API to create and terminate EMR clusters, scale clusters using Auto Scaling or manual resizing, and submit and run Apache Spark, Apache Hive, or Apache Pig workloads. These decisions are often triggered from cluster state-related information. Previously, […]

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Implementing Authorization and Auditing using Apache Ranger on Amazon EMR

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 Directory users and groups. They would like to manage it on a central security policy server and […]

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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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