MapR Enables Hadoop-as-a-Service with Multi-tenancy, Security and End-to-End Management
San Jose, CA

Version 2.0 benefits on-premise, cloud and hybrid deployments
 
MapR Technologies, Inc., the provider of the open, enterprise-grade distribution for Apache Hadoop, today announced Version 2.0 of the MapR Distribution which includes advanced monitoring, management, isolation and security for Hadoop. The latest version enables organizations to meet the needs of multiple users, groups and applications within the same cluster. In a related announcement, MapR also announced the availability of the MapR M3 and M5 editions through Amazon Web Services.
 
MapR provides complete visibility into all cluster activities. Every node captures and reports node, job and task metrics. The MapR Control System (MCS) displays this information in dozens of views, ranging from interactive historgrams to time charts, allowing administrators to filter, aggregate and drill-down on individual jobs and tasks. All MapReduce log files are logically centralized so they can be instantly accessed, searched and analyzed. All metrics and logs are automatically compressed, sharded and replicated in MapR's highly-available storage layer, and users can easily perform custom analytics with MapReduce, Hive, Pig or Cascading.
 
"Whether you're deploying on-premise, in the cloud, or a hybrid model for disaster recovery or elastic deployments, MapR has optimized the management and performance to ensure an easy and successful deployment," said Jack Norris, vice president of marketing, MapR Technologies. "Customers continue to benefit from MapR's innovations providing ease of use, dependability and increased performance."
 
With 2.0, MapR also provides advanced job management capabilities enabling an administrator to have complete control over the operation of the cluster, jobs and tasks. Job and data placement control ensures that data and job execution can be isolated in different areas of a cluster for performance, security or cost control. MapR now provides complete end-to-end visibility and control of hardware, software, storage, MapReduce and other components of the MapR Distribution.
 
Additional advances in Version 2.0 include:

  • Job monitoring and management A graphical display of time and resources consumed by jobs and tasks lets users allocate and track cluster usage, diagnose slow nodes and determine real performance metrics. The MapR Control System displays line charts and histograms that let administrators zoom in to see detailed information on a large variety of job and task statistics.
  • Job and data placement control Job placement control lets users specify exactly which nodes will run each job to take advantage of different performance profiles or limit jobs to specific physical locations. Powerful wildcard syntax lets users define groups of nodes and assign jobs to any combination of groups. Administrators can leverage MapReduce queues and ACLs to restrict specific users and groups to a subset of the nodes in the cluster. Administrators can also control data placement, enabling them to isolate specific datasets to a subset of the nodes.
  • Multi-tenancy support MapR clusters provide powerful features to logically partition a physical cluster to provide separate administration, data placement, job execution and network access.
  • Central configuration With MapR, users don't have to tune MapReduce node by node. Custom central configuration can be applied to all nodes or only the nodes of the administrators choosing, and updated as frequently as desired.
  • Central logging Logical centralization of log data makes it easy to diagnose problems such as failed jobs without searching from node to node and manually aggregating logs. MapR does this through the logical aggregation of local volumes on each node, saving time and eliminating the unnecessary copying or moving of data.
  • Enhanced compression MapR now provides compression algorithms to let users choose how to store data in the cluster. With LZ4, LZF and GZIP algorithms, MapR gives the flexibility to balance performance versus space saved.
  • Enhanced security With MapR, administrators don't need to worry about their data. MapR provides security throughout the MapReduce stack, ranging from IP address whitelisting to a secured TaskTracker and integration with PAM. In addition, MapR now supports SELinux.
  • New versions of HBase™, Hive, Pig and other open source components MapR continues to update the distribution with the latest versions of Hadoop components from the broad Hadoop ecosystem. These components are part of the tested, hardened and supported MapR Distribution. This release features new versions of Hive, HBase™ and many other open source components.
  • SUSE support MapR is now supported on three of the most popular enterprise-ready Linux distributions: Ubuntu, Red Hat Enterprise Linux (RHEL) and SUSE Linux Enterprise Server (SLES).

The Version 2.0 beta for the M3 and M5 editions of the MapR Distribution are now available for download at www.mapr.com/download.
{loadposition boilerplate}


About MapR Technologies

About MapR Technologies

MapR enables organizations to create disruptive advantage and long-term value from their data with the industry’s only Converged Data Platform, which delivers distributed processing, real-time analytics, and enterprise-grade requirements across cloud and on-premise environments–while leveraging the significant ongoing development in open source technologies including Spark and Hadoop. Organizations with the most demanding production needs, including sub-second response for fraud prevention, secure and highly available data-driven insights for better healthcare, petabyte analysis for threat detection, and integrated operational and analytic processing for improved customer experiences, run on MapR. A majority of customers achieves payback in fewer than 12 months and realizes greater than 5X ROI. MapR ensures customer success through world-class professional services and with free on-demand training that 50,000 developers, data analysts and administrators have used to close the big data skills gap. Amazon, Cisco, Google, HPE, Microsoft, SAP, and Teradata are part of the worldwide MapR partner ecosystem. Investors include Future Fund, Google Capital, Lightspeed Venture Partners, Mayfield Fund, NEA, Qualcomm Ventures and Redpoint Ventures. Connect with MapR on LinkedIn, and Twitter.

Media Contacts

Beth Winkowski
MapR Technologies, Inc.
(978) 649-7189
bwinkowski@maprtech.com

Kim Pegnato
MapR Technologies, Inc.
(781) 620-0016
kpegnato@maprtech.com

www.mapr.com/company/press-releases