AWS Big Data Blog

Category: Analytics

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 […]

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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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Derive Insights from IoT in Minutes using AWS IoT, Amazon Kinesis Firehose, Amazon Athena, and Amazon QuickSight

Ben Snively is a Solutions Architect with AWS Speed and agility are essential with today’s analytics tools. The quicker you can get from idea to first results, the more you can experiment and innovate with your data, perform ad-hoc analysis, and drive answers to new business questions. Serverless architectures help in this respect by taking […]

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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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Building an Event-Based Analytics Pipeline for Amazon Game Studios’ Breakaway

All software developers strive to build products that are functional, robust, and bug-free, but video game developers have an extra challenge: they must also create a product that entertains. When designing a game, developers must consider how the various elements—such as characters, story, environment, and mechanics—will fit together and, more importantly, how players will interact […]

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Using SaltStack to Run Commands in Parallel on Amazon EMR

Miguel Tormo is a Big Data Support Engineer in AWS Premium Support Amazon EMR provides a managed Hadoop framework that makes it easy, fast, and cost-effective to process vast amounts of data across dynamically scalable Amazon EC2 instances. Amazon EMR defines three types of nodes: master node, core nodes, and task nodes. It’s common to […]

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Joining and Enriching Streaming Data on Amazon Kinesis

Are you trying to move away from a batch-based ETL pipeline? You might do this, for example, to get real-time insights into your streaming data, such as clickstream, financial transactions, sensor data, customer interactions, and so on.  If so, it’s possible that as soon as you get down to requirements, you realize your streaming data […]

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Amazon Redshift Engineering’s Advanced Table Design Playbook: Table Data Durability

Part 1: Preamble, Prerequisites, and Prioritization Part 2: Distribution Styles and Distribution Keys Part 3: Compound and Interleaved Sort Keys Part 4: Compression Encodings Part 5: Table Data Durability (Translated into Japanese) In the fifth and final installment of the Advanced Table Design Playbook, I’ll discuss how to use two simple table durability properties to […]

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Interactive Analysis of Genomic Datasets Using Amazon Athena

Aaron Friedman is a Healthcare and Life Sciences Solutions Architect with Amazon Web Services The genomics industry is in the midst of a data explosion. Due to the rapid drop in the cost to sequence genomes, genomics is now central to many medical advances. When your genome is sequenced and analyzed, raw sequencing files are […]

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