Platform Computing announced the availability of Platform
MapReduce, the industry’s first enterprise-class, distributed runtime engine
for MapReduce applications.
Platform MapReduce
enables enterprise businesses to focus on moving MapReduce applications into
production by providing enterprise-class manageability and scale, high resource
utilization and availability, ease of operation, multiple application support
and an open distributed file system architecture, including immediate support
for Hadoop Distributed File System (HDFS) and Appistry Cloud IQ, with
additional support coming soon.
Platform MapReduce is built on the company’s core
technologies, Platform LSF and Platform Symphony, currently powering the most
demanding, mission-critical distributed computing workloads at Fortune 500
companies across a variety of industries.
Platform MapReduce is an enterprise-class, distributed
runtime engine for MapReduce applications that schedules and manages MapReduce
applications in a cluster across an entire distributed file system.
Although many organizations today see the promise of open
source MapReduce solutions, they are reluctant to deploy an open source,
distributed runtime engine for enterprise applications because they lack the
ability to scale or manage large, distributed environments and workloads while
maintaining service levels or avoiding vendor lock-in.
Designed to help organizations overcome the barriers of
moving MapReduce applications into production, Platform MapReduce is based on
Platform Computing’s industry leading experience managing distributed
architectures for nearly two decades and is well suited to provide
enterprise-class, runtime services across a distributed file system.
Platform MapReduceincludes policy driven workload
scheduling, tuning, monitoring, and automated administration; scales up to
20,000 servers, 40,000 cores and supports 10,000 concurrent jobs and 300,000
concurrent tasks – exceeds all other MapReduce distributed runtime engines.
Policy driven workload scheduling to allow organizations
to do more with less. Provides up to 10,000 priority levels to ensure high resource
utilization, allowing more applications to access shared data.
Platform MapReduceguarantees uptime within the
distributed runtime engine. By providing automated failover for map tasks,
reduce tasks and name nodes, there is no single point of failure; these
capabilities are lacking in alternative solutions.
Supports applications running different versions on the
same cluster, eliminating the need for IT to reconfigure or upgrade resources
to adapt various versions.
Platform MapReduce runs multiple MapReduce applications
on a shared cluster; supports applications running different versions on the
same cluster and supports multiple files systems, including Hadoop Distributed
File System (HDFS) and Appistry Cloud IQ, with additional support coming soon.
“High-Performance Analytics a SAS specialty happens
at the intersection of Big Data and High-Performance Computing. Our mutual
customers have benefited from Platform’s expertise and unique capabilities to
manage and support these complex, distributed clusters. Platform MapReduce is a
welcome addition to the rapidly evolving Hadoop ecosystem. Platform Computing
can play a critical role in the evolution and adoption of Hadoop in the
Enterprise,” said Paul Kent, SAS vice president, Platform Research and Development.
By Telecomlead.com Team
editor@telecomlead.com
