April Newsletter: MapR Adds Complete Apache Spark Stack

We just wrapped up a great quarter for MapR! We introduced our free Sandbox for Hadoop, achieved the highest ranking for Current Offering in a Big Data Hadoop Solutions report by Forrester, and announced the MapR Distribution for Hadoop with YARN and HP Vertica.

Read about our latest announcements, top blog posts, webinars, white papers and more in this information-packed newsletter.

Top Stories

Apache Spark

MapR Adds Complete Apache Spark Stack to its Distribution for Hadoop  

MapR announced a strategic partnership with Databricks and the addition of the complete Apache Spark technology stack to the MapR Distribution. The Spark in-memory processing framework provides speed, programming ease and real-time processing advantages. Learn more >

Amazon web services (AWS)Hadoop in the Cloud: Unlocking the Potential of Big Data on AWS

In this recorded webinar, we show you how to run Tableau queries through either Impala or Hive on Amazon Web Services. You will learn how you can free yourself from the heavy lifting required to run Hadoop on-premise, and gain the advantages of using the cloud to increase flexibility and accelerate projects while lowering costs. Watch video >

Top Blog Posts

Maximizing Big Data Performance and Scalability with MapR and Cisco UCS 
Enterprises are turning to big data and Hadoop in order to improve business performance and provide a competitive advantage. But to unlock business value from data quickly, easily and cost-effectively, organizations need to find and deploy a truly reliable Hadoop infrastructure that can perform, scale, and be used safely for mission-critical applications. Read more >

5 Steps to Avoiding Java Heap Space Errors 
Keeping these five steps in mind can save you a lot of headaches and avoid Java heap space errors.

  1. Calculate memory needed.
  2. Check that the JVMs have enough memory for the TaskTracker tasks.
  3. Check that the JVMs settings are suitable for your tasks.
  4. Limit your nodes use of swap space and paged memory.
  5. Set the task attempt slots to a number that’s lower than the number calculated by the JobTracker web GUI.

Read more >

Basic Notes on Configuring Eclipse as a Hadoop Development Environment for MapR
Eclipse is a popular development tool and we thought it would be helpful to share some tips on using Eclipse with MapR to write MapReduce programs. The following notes describe how to enable Eclipse as your development IDE for Hadoop MapReduce jobs using an existing MapR cluster. In this post you will learn how to: install eclipse, install and configure the MapR client, optionally install and configure an NFS clients, configure a few things in Eclipse, create a project, and use the built in Eclipse application launcher to launch your MapReduce jobs from inside Eclipse. Read more >

Upcoming Webinars

Together, MapR and our partners help organizations like yours make sense of your ever-expanding variety and volume of unstructured, semi-structured, and structured data. Check out our partner webinars and discover how you can get more from Hadoop.

CenturyLink Fast and Furious: From POC to an Enterprise Big Data Stack in 2014
Thursday, April 24, 2014, 11:00 am PT / 2:00 pm ET

Join CenturyLink Technology Solutions (Formerly Savvis) and MapR as we deconstruct and demystify “the enterprise big data stack.” We will provide you with a more holistic view of the landscape, explore use cases to show how you can derive business value from it, and share best practices for navigating through the fragmented big data environment. Register now >

MapR and DatabricksLet Spark Fly: Advantages and Use Cases for Spark on Hadoop
Tuesday, April 29, 2014, 9:00 am PT / 12:00 pm ET 

Join MapR and Databricks, the company that created and led the development of the Spark stack, as we cut through the noise to uncover practical advantages for having the full set of Spark technologies at your disposal and reveal the benefits for running Spark on Hadoop. Register now >

MapR Academy

MapReduce Programming in Java

  • When: April 28-30 & June 10-12
  • Where: Sunnyvale, CA

This course is designed to teach developers how to write effective MapReduce applications. The primary objective of the training is to understand how to write effective MapReduce applications in Java, and every lesson and lab contributes to that performance objective. The course also covers debugging, managing jobs, improving performance, working with custom data, managing workflows, and using other programming languages for MapReduce. Sign up >

MapR In The News

MapR Technologies Expands Big Data Search with Elasticsearch
MapR Technologies announced the integration of Elasticsearch’s real-time search and analytics capability with its distribution for Hadoop, enabling customers to search and store tremendous amounts of information in real time. Read more >

WE-Ankor Signs Partnership Agreement with MapR Technologies in Israel
Highly respected, integrated IT infrastructure solutions specialist selects MapR as technology foundation for newly established big data practice. Read more >

Upcoming Events

Machine learning, large scale data analytics, Apache Hadoop, NoSQL databases, and much more are coming this April. No matter what you’re interested in, we have a schedule jammed packed full of great events for you. View our list of upcoming events >


Streaming Data Architecture:

New Designs Using Apache Kafka and MapR Streams




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