AWS Big Data Blog

Category: Analytics

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 […]

Read More

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 […]

Read More

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 […]

Read More

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 […]

Read More

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 […]

Read More

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 […]

Read More

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 […]

Read More

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 […]

Read More

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 […]

Read More

Powering Amazon Redshift Analytics with Apache Spark and Amazon Machine Learning

Air travel can be stressful due to the many factors that are simply out of airline passengers’ control. As passengers, we want to minimize this stress as much as we can. We can do this by using past data to make predictions about how likely a flight will be delayed based on the time of […]

Read More