Platform MapReduce Provides Enterprise-Class MapReduce Distributed Runtime Engine across Multiple Distributed File Systems to Improve ROI
Platform Computing, the leader in cluster, grid and cloud management software, today 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 MapReduce Key Features
• Enterprise-Class Manageability and Scale – includes 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.
• High Resource Utilization and Sharing – includes 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.
• High Availability - guarantees 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.
• Easy to Operate – supports applications running different versions on the same cluster, eliminating the need for IT to reconfigure or upgrade resources to adapt various versions.
• Multiple Application Support – runs multiple MapReduce applications on a shared cluster; supports applications running different versions on the same cluster.
• Open Distributed File System Architecture – supports multiple files systems, including Hadoop Distributed File System (HDFS) and Appistry Cloud IQ, with additional support coming soon.
• The company is also offering support services for HDFS that can be integrated with Platform MapReduce as a single-source distribution option for Platform customers.
Platform Computing’s solutions are already in use in key markets where MapReduce will be required. Early adopters span various industry verticals including financial services, government, retail and life sciences. Platform also has a strong foothold in key ISV customer bases in the business intelligence market, such as SAS customers. Some ideal MapReduce application use cases include:
• Financial Services - Compliance and regulatory reporting, fraud detection and security analytics, credit scoring and analysis, and trade surveillance.
• Telecommunications – Revenue assurance and price optimization, customer churn prevention, and Call Detail Record (CDR) analysis.
• Government Agencies – Fraud detection and cyber-security, compliance and regulatory analysis, and energy consumption and carbon footprint management.
• Life Sciences – Drug discovery and development analysis, and genome sequencing analysis.
• “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,” said Paul Kent, SAS Vice President of Platform Research and Development. “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.”
• “We see many companies experimenting with open source Hadoop, but also an opportunity for complementary enterprise-focused products that deliver the ability to manage and process unstructured data efficiently for making critical business decisions,” said Matt Aslett, Senior Analyst Enterprise Software, The 451 Group. “Platform Computing’s expertise in workload management and distributed computing environments appears to be a natural fit for the MapReduce programming model and scaling to support extremely large data sets."
• “The enterprise analytics market is ripe for the management and workload capabilities that Platform brings to the table through our recognized leadership in high performance computing,” said Ken Hertzler, Vice President, Product Marketing, Platform Computing. “With Platform MapReduce, we are helping companies lower their operational cost of analysis with a MapReduce distributed runtime engine that they need to get analysis done faster and cheaper in order to get the most out of their data.”
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