AWS Big Data Blog
Tag: Amazon EMR
Turning Amazon EMR into a Massive Amazon S3 Processing Engine with Campanile
Michael Wallman is a senior consultant with AWS ProServ Have you ever had to copy a huge Amazon S3 bucket to another account or region? Or create a list based on object name or size? How about mapping a function over millions of objects? Amazon EMR to the rescue! EMR allows you to deploy large […]
Running an External Zeppelin Instance using S3 Backed Notebooks with Spark on Amazon EMR
Dominic Murphy is an Enterprise Solution Architect with Amazon Web Services Apache Zeppelin is an open source GUI which creates interactive and collaborative notebooks for data exploration using Spark. You can use Scala, Python, SQL (using Spark SQL), or HiveQL to manipulate data and quickly visualize results. Zeppelin notebooks can be shared among several users, […]
Securely Access Web Interfaces on Amazon EMR Launched in a Private Subnet
Ben Snively is a Solutions Architect with AWS Private subnets allow you to limit access to deployed components, and to control security and routing of the system. You can also use a private subnet to connect an on-premises local network to AWS through a VPN or AWS Direct Connect. Amazon EMR allows customers to launch […]
Analyze Data with Presto and Airpal on Amazon EMR
Songzhi Liu is a Professional Services Consultant with AWS You can now launch Presto version 0.119 on Amazon EMR, allowing you to easily spin up a managed EMR cluster with the Presto query engine and run interactive analysis on data stored in Amazon S3. You can integrate with Spot instances, publish logs to an S3 […]
Using BlueTalon with Amazon EMR
This is a guest post by Pratik Verma, Founder and Chief Product Officer at BlueTalon. Leonid Fedotov, Senior Solution Architect at BlueTalon, also contributed to this post. Amazon Elastic MapReduce (Amazon EMR) makes it easy to quickly and cost-effectively process vast amounts of data in the cloud. EMR gets used for log, financial, fraud, and […]
Integrating Amazon Kinesis, Amazon S3 and Amazon Redshift with Cascading on Amazon EMR
This is a guest post by Ryan Desmond, Solutions Architect at Concurrent. Concurrent is an AWS Advanced Technology Partner. With Amazon Kinesis developers can quickly store, collate and access large, distributed data streams such as access logs, click streams and IoT data in real-time. The question then becomes, how can we access and leverage this […]
How Expedia Implemented Near Real-time Analysis of Interdependent Datasets
This is a guest post by Stephen Verstraete, a manager at Pariveda Solutions. Pariveda Solutions is an AWS Premier Consulting Partner. Common patterns exist for batch processing and real-time processing of Big Data. However, we haven’t seen patterns that allow us to process batches of dependent data in real-time. Expedia’s marketing group needed to analyze […]
Large-Scale Machine Learning with Spark on Amazon EMR
This is a guest post by Jeff Smith, Data Engineer at Intent Media. Intent Media, in their own words: “Intent Media operates a platform for advertising on commerce sites. We help online travel companies optimize revenue on their websites and apps through sophisticated data science capabilities. On the data team at Intent Media, we are […]
Test drive two big data scenarios from the ‘Building a Big Data Platform on AWS’ bootcamp
Matt Yanchyshyn is a Sr. Manager for AWS Solutions Architecture AWS offers a number of events during the year such as our annual AWS re:Invent conference, the AWS Summit series, the AWS Pop-up Loft, and a variety of roadshows. All of these provide opportunities for AWS customers to attend talks focused on big data and […]
Indexing Common Crawl Metadata on Amazon EMR Using Cascading and Elasticsearch
Hernan Vivani is a Big Data Support Engineer for Amazon Web Services A previous post showed you how to get started with Elasticsearch and Kibana on Amazon EMR. In that post, we installed Elasticsearch and Kibana on an Amazon EMR cluster using bootstrap actions. This post shows you how to build a simple application with […]