Posted on May 4, 2016 by Nick Amato

If you’ve had a chance to work with Hadoop or Spark a little, you probably already know that HDFS doesn't support full random read-writes or many other capabilities typically required in a production-ready file system.


Posted on April 19, 2016 by Nicolas Perez

In this blog post, you'll learn how to do some simple, yet very interesting analytics that will help you solve real problems by analyzing specific areas of a social network. Using a subset of a Twitter stream was the perfect choice to use in this demonstration...

Posted on May 3, 2016 by Carol McDonald

In this post we are going to discuss building a real time solution for credit card fraud detection.

Posted on May 2, 2016 by Jim Scott

In some circles today there is a sort of ‘Hadoop vs. RDBMS’ debate ongoing. Often the discussion casts Hadoop as the obvious heir apparent in the data processing world, with RDBMS cast as your father’s Oldsmobile.

Posted on April 29, 2016 by Mathieu Dumoulin

There are many options for monitoring the performance and health of a MapR cluster. In this post, I will present the lesser-known method for monitoring the CLDB using the Java Management Extensions (JMX).

Posted on April 28, 2016 by Crystal Valentine

Technological innovation is one of the great stories of the 21st century. Over the past 15 years, technology companies have generated unprecedented wealth at a blistering pace, fueled by smart and capable teams of brilliant scientists and engineers.

Posted on April 27, 2016 by Mathieu Dumoulin

We have experimented with on a 5 node MapR 5.1 cluster running Spark 1.5.2 and will share our experience, difficulties, and solutions on this blog post.

Posted on April 27, 2016 by Sean O’Dowd

We are honored to announce that MapR was named one of the Top 10 Banking Analytics Solution Providers for 2016 by Banking CIO Outlook magazine.

Posted on April 25, 2016 by Ellen Friedman

Organizations embracing big data are ready to put data to work, including looking for ways to effectively analyze data from a variety of sources in real time or near real time.

Posted on April 22, 2016 by Carol McDonald

This post will show how to integrate Apache Spark Streaming, MapR-DB, and MapR Streams for fast, event-driven applications.

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Featured Author

Data Engineer, MapR
Mathieu is a Data Engineer on the MapR Professional Services team, and is based in the Asia-Pacific region.

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