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.
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...
In this post we are going to discuss building a real time solution for credit card fraud detection.
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.
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).
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.
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.
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.
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.
This post will show how to integrate Apache Spark Streaming, MapR-DB, and MapR Streams for fast, event-driven applications.
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