AWS Big Data Blog

Category: Analytics*

Analyze Security, Compliance, and Operational Activity Using AWS CloudTrail and Amazon Athena

As organizations move their workloads to the cloud, audit logs provide a wealth of information on the operations, governance, and security of assets and resources. As the complexity of the workloads increases, so does the volume of audit logs being generated. It becomes increasingly difficult for organizations to analyze and understand what is happening in […]

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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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Scheduled Refresh for SPICE Data Sets on Amazon QuickSight

Jose Kunnackal is a Senior Product Manager for Amazon Quicksight This blog post has been translated into Japanese. In November 2016, we launched Amazon QuickSight, a cloud-powered, business analytics service that lets you quickly and easily visualize your data. QuickSight uses SPICE (Super-fast, Parallel, In-Memory Calculation Engine), a fully managed data store that enables blazing […]

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Create Tables in Amazon Athena from Nested JSON and Mappings Using JSONSerDe

Most systems use Java Script Object Notation (JSON) to log event information. Although it’s efficient and flexible, deriving information from JSON is difficult. In this post, you will use the tightly coupled integration of Amazon Kinesis Firehose for log delivery, Amazon S3 for log storage, and Amazon Athena with JSONSerDe to run SQL queries against these logs without […]

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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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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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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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Create a Healthcare Data Hub with AWS and Mirth Connect

As anyone visiting their doctor may have noticed, gone are the days of physicians recording their notes on paper. Physicians are more likely to enter the exam room with a laptop than with paper and pen. This change is the byproduct of efforts to improve patient outcomes, increase efficiency, and drive population health. Pushing for […]

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Serving Real-Time Machine Learning Predictions on Amazon EMR

by Derek Graeber and Guy Ernest | on | in Amazon EMR* | Permalink | Comments |  Share

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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