AWS Big Data Blog

AWS Big Data is Coming to HIMSS!

The AWS Big Data team is coming to HIMSS, the industry-leading conference for professionals in the field of healthcare technology. The conference brings together more than 40,000 health IT professionals, clinicians, administrators, and vendors to talk about the latest innovations in health technology. Because transitioning healthcare to the cloud is at the forefront of this […]

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Migrate External Table Definitions from a Hive Metastore to Amazon Athena

For customers who use Hive external tables on Amazon EMR, or any flavor of Hadoop, a key challenge is how to effectively migrate an existing Hive metastore to Amazon Athena, an interactive query service that directly analyzes data stored in Amazon S3. With Athena, there are no clusters to manage and tune, and no infrastructure to […]

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Implement Serverless Log Analytics Using Amazon Kinesis Analytics

Applications log a large amount of data that—when analyzed in real time—provides significant insight into your applications. Real-time log analysis can be used to ensure security compliance, troubleshoot operation events, identify application usage patterns, and much more. Ingesting and analyzing this data in real time can be accomplished by using a variety of open source […]

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Month in Review: January 2017

Another month of big data solutions on the Big Data Blog! Take a look at our summaries below and learn, comment, and share. Thank you for reading! NEW POSTS Decreasing Game Churn: How Upopa used ironSource Atom and Amazon ML to Engage Users Ever wondered what it takes to keep a user from leaving your […]

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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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Run Mixed Workloads with Amazon Redshift Workload Management

This blog post has been translated into Japanese.  Mixed workloads run batch and interactive workloads (short-running and long-running queries or reports) concurrently to support business needs or demand. Typically, managing and configuring mixed workloads requires a thorough understanding of access patterns, how the system resources are being used and performance requirements. It’s common for mixed […]

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Converging Data Silos to Amazon Redshift Using AWS DMS

Organizations often grow organically—and so does their data in individual silos. Such systems are often powered by traditional RDBMS systems and they grow orthogonally in size and features. To gain intelligence across heterogeneous data sources, you have to join the data sets. However, this imposes new challenges, as joining data over dblinks or into a […]

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Call for Papers! DEEM: 1st Workshop on Data Management for End-to-End Machine Learning

Amazon and Matroid will hold the first workshop on Data Management for End-to-End Machine Learning (DEEM) on May 14th, 2017 in conjunction with the premier systems conference SIGMOD/PODS 2017 in Raleigh, North Carolina. For more details about the workshop focus, see Challenges and opportunities in machine learning below. DEEM brings together researchers and practitioners at […]

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Decreasing Game Churn: How Upopa used ironSource Atom and Amazon ML to Engage Users

This is a guest post by Tom Talpir, Software Developer at ironSource. ironSource is as an Advanced AWS Partner Network (APN) Technology Partner and an AWS Big Data Competency Partner. Ever wondered what it takes to keep a user from leaving your game or application after all the hard work you put in? Wouldn’t it be great […]

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