AWS Big Data Blog

Introduction to Python UDFs in Amazon Redshift

Christopher Crosbie is a Healthcare and Life Science Solutions Architect with Amazon Web Services When your doctor takes out a prescription pad at your yearly checkup, do you ever stop to wonder what goes into her thought process as she decides on which drug to scribble down? We assume that journals of scientific evidence coupled […]

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 […]

Building and Maintaining an Amazon S3 Metadata Index without Servers

Mike Deck is a Solutions Architect with AWS Amazon S3 is a simple key-based object store whose scalability and low cost make it ideal for storing large datasets. Its design enables S3 to provide excellent performance for storing and retrieving objects based on a known key. Finding objects based on other attributes, however, requires doing […]

Implementing Efficient and Reliable Producers with the Amazon Kinesis Producer Library

Kevin Deng is an SDE with the Amazon Kinesis team and is the lead author of the Amazon Kinesis Producer Library How do you vertically scale an Amazon Kinesis producer application by 100x? While it’s easy to get started with streaming data into Amazon Kinesis, streaming large volumes of data efficiently and reliably presents some […]

Connecting R with Amazon Redshift

Markus Schmidberger is a Senior Big Data Consultant for AWS Professional Services Amazon Redshift is a fast, petabyte-scale cloud data warehouse for PB of data. AWS customers are moving huge amounts of structured data into Amazon Redshift to offload analytics workloads or to operate their DWH fully in the cloud. Business intelligence and analytic teams […]

Running R on AWS

Many AWS customers already use the popular open-source statistic software R for big data analytics and data science. Other customers have asked for instructions and best practices for running R on AWS. Several months ago, I (Markus) wrote a post showing you how to connect R with Amazon EMR, install RStudio on the Hadoop master node, and use R […]

Presto-Amazon Kinesis Connector for Interactively Querying Streaming Data

This is a guest post by Sivaramakrishnan Narayanan, Member of Technical Staff at Qubole, and Xing Quan, Director of Product Management at Qubole. Qubole is an AWS Advanced Technology Partner. Amazon Kinesis is a scalable and fully managed service for streaming large, distributed data sets. As applications (particularly on mobile and wearable devices) start to […]

Building Scalable and Responsive Big Data Interfaces with AWS Lambda

This is a guest post by Martin Holste, a co-founder of the Threat Analytics Platform at FireEye where he is a senior researcher specializing in prototypes. Overview At FireEye, Inc., we process billions of security events every day with our Threat Analytics Platform, running on AWS. In building our platform, one of the problems we […]

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 […]