Sign up for our newsletter and get the latest big data news and analysis.

MapR Technologies Launches Industry’s First Apache Hadoop Application Gallery

MapR Logo - New 2014insideBIGDATA Hadoop Summit 2014 Coverage

MapR Technologies, Inc., provider of a leading distribution for Apache Hadoop, today launched at Hadoop Summit the industry’s first Hadoop application gallery. Launching with solutions from a wide range of Hadoop ecosystem partners, the MapR App Gallery is designed to help customers derive greater business value from big data as they scale-out their enterprise data architectures.

Aimed at end-user developers, administrators, and analysts who want to develop more sophisticated big data capabilities to enhance their business operations and decision-making, the MapR App Gallery offers the benefit of ready-made big data utilities and applications.

Our customers typically run many different applications on a MapR cluster and the App Gallery makes it much easier and faster to achieve success,” said Jack Norris, chief marketing officer, MapR Technologies.  “There is already a broad range of applications available, and we anticipate the number of applications will grow – particularly those leveraging unique capabilities that enable customers to better optimize revenue, control costs and mitigate risk.”

The App Gallery includes administrator-oriented apps for provisioning, management, and security; developer-oriented applications, query engines and frameworks; and analyst-focused applications for business intelligence and machine learning.  Partners and solutions represented in the MapR App Gallery today include:

  • Database: Hadapt, HP Vertica, Rainstor, and Splice Machine
  • Analytics and Data Integration: Alpine Data Labs, Appfluent, Data Tactics, Datameer, DataTorrent, Informatica, Information Builders, Jaspersoft, Pentaho, Platfora, Revelytics, Revolution Analytics, Syncsort, Tableau, and Talend
  • Search: Elasticsearch and LucidWorks
  • Machine Learning:  0xdata, Skytree, and Zementis
  • Management and security: Dataguise, Splunk, StackIQ, and Voltage

The MapR Distribution is recognized to be an open distribution for Hadoop with industry-standard APIs to make it plug-and-play without special-purpose connectors or porting.  Developers are encouraged to participate by leveraging one or more of the following open interfaces in their applications:

  • Storage access via HDFS API and NFS
  • Job execution via YARN, Tez, MapReduce, and Spark
  • Ecosystem interfaces including HBase, HiveQL, PigLatin
  • SQL via Drill, Hive, Stinger/Tez
  • Administration interfaces including REST

App Gallery contributors verify that their applications interoperate with the MapR Distribution with documentation and support information provided for each application. Developers can submit new applications directly to the App Gallery HERE.

With our comprehensive platform for big data integration and analytics, we’re proud to be involved in the launch of the new MapR App Gallery,” said Eddie White, EVP of business development at Pentaho. “Companies interested in generating greater business value from their MapR Hadoop investment can turn to this resource for exploring a wide range of best-in-class applications and tools that interoperate with the MapR Distribution.”

Please visit MapR at booth P2 during Hadoop Summit taking place at the San Jose Convention Center from June 3-5.

 

Sign up for the free insideBIGDATA newsletter.

Comments

  1. So, the term “App” sounds kind of a small, not big of deal thing. Like the “Apps” in Apple’s App Store. But some of the items in the list like Datameer or Tableau are big of a deal. Yes, they are applications than can be run on top of distributions like MAPR, but it is not fair to put them in a small “App Store”. What you think?

    • Hadoop Developer says:

      There are big and small apps on the Apple AppStore as well. What helps is this one place from where you can see all the directly supported applications that can run on MapR. Makes it easier to take a decision on what to try out, rather than getting something that might not be able to leverage the full capabilities of the MapR architecture.

Resource Links: