AWS Big Data Blog

Using AWS Data Pipeline’s Parameterized Templates to Build Your Own Library of ETL Use-case Definitions

Leena Joseph is an SDE for AWS Data Pipeline In an earlier post, we introduced you to ETL processing using AWS Data Pipeline and Amazon EMR. This post shows how to build ETL workflow templates with AWS Data Pipeline, and build a library of recipes to implement common use cases. This is an introduction to […]

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Building a Numeric Regression Model with Amazon Machine Learning

Guy Ernest is a Solutions Architect with AWS We need to predict future values in our businesses. These predictions are important for better planning of resource allocation and making other business decisions. Often, we settle for a simplified heuristic of average values from the past and some change assumption because more accurate alternatives are too […]

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Launching and Running an Amazon EMR Cluster in your VPC – Part 2: Custom DNS

Daniel Garrison is a Big Data Support Engineer for Amazon Web Services In Part 1 you learned how Amazon EMR uses Amazon VPC DNS hostname and DHCP settings to satisfy the Hadoop requirements. Because it’s common to change the domain name setting in your DHCP options set to a custom internal domain name, this post […]

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Nasdaq’s Architecture using Amazon EMR and Amazon S3 for Ad Hoc Access to a Massive Data Set

This is a guest post by Nate Sammons, a Principal Architect for Nasdaq The Nasdaq group of companies operates financial exchanges around the world and processes large volumes of data every day. We run a wide variety of analytic and surveillance systems, all of which require access to essentially the same data sets. The Nasdaq […]

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Processing Amazon Kinesis Stream Data Using Amazon KCL for Node.js

Manan Gosalia is an SDE for Amazon Kinesis This blog post shows you how to get started with the Amazon Kinesis Client Library (KCL) for Node.js. The Node.js framework uses an event-driven, non-blocking I/O model that makes it lightweight, efficient, and perfect for data-intensive, real-time applications that run across distributed devices. JavaScript is also simple […]

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Streaming Analytics with DataTorrent RTS and Amazon EMR

Nick Durkin is a Senior Solution Engineer for DataTorrent. DataTorrent is an AWS Technology Partner. In this blog post, we introduce fast big data and provide context about the DataTorrent RTS streaming analytics platform. In addition, we show you how to implement a real-time, streaming analytics application for capturing social media trends from Twitter using […]

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Launching and Running an Amazon EMR Cluster inside a VPC

NOTE: This article contains information and instructions only pertinent to older EMR releases (emr-4.6.0 and earlier) and may no longer be applicable.  For latest information please refer to the current user guide. Daniel Garrison is a Big Data Support Engineer for Amazon Web Services Introduction With Amazon EC2 now firmly in the VPC-by-default model, it’s […]

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Using Amazon EMR and Hunk for Rapid Response Log Analysis and Review

Patrick Shumate is a Solutions Architect for AWS. Introduction It is fairly common to collect access and application logs but never interactively review them. Monitoring dashboards, coupled with well-instrumented applications, allow operators to manage day-to-day operations without ever digging into the flood of logs silently stored in Amazon S3. That works until the monitoring dashboard […]

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A Zero-Administration Amazon Redshift Database Loader

Ian Meyers is a Solutions Architecture Senior Manager with AWS With this new AWS Lambda function, it’s never been easier to get file data into Amazon Redshift. You simply push files into a variety of locations on Amazon S3 and have them automatically loaded into your Amazon Redshift clusters. Using AWS Lambda with Amazon Redshift […]

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Using IPython Notebook to Analyze Data with Amazon EMR

Manjeet Chayel is a Solutions Architect with AWS IPython Notebook is a web-based interactive environment that lets you combine code, code execution, mathematical functions, rich documentation, plots, and other elements into a single document. In the background, IPython Notebook stores this information as a JSON document. The main advantage of a notebook when compared to […]

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