Platform Computing Supports MapReduce
Cloud and grid software provider Platform Computing has announced support for the Apache Hadoop MapReduce programming model.Platform Computing, a provider of cluster, grid and cloud management software, has announced support for the Apache Hadoop MapReduce programming model. Platform officials said the company is bringing enterprise-class distributed computing to business analytics applications that process "big" data using MapReduce. Based on more than 18 years of industry leadership in workload management for HPC (high performance computing) applications, Platform Computing's analytics solutions are a natural expansion of the company's distributed computing experience built on the company's core technologies, Platform LSF and Platform Symphony, the company said.
As "big data" has increased, the need for analytics platforms that can support distributed environments at high reliability, availability, scale and manageability to perform business analytics in a timely manner has increased, Platform officials said. Thus, today, companies need analytics that can perform at the speed of business in order to make the best business decisions possible.
"Many of Platform's customers already use our products to run complex analytics and other distributed workload services," said Ken Hertzler, vice president of product management at Platform Computing, in a statement. "Platform is perfectly positioned to run enterprise-class distributed workload for MapReduce applications. Our products are architected from the outset to service large-scale parallel processing on commodity infrastructures. The solutions are also designed to work specifically with multiple distributed file systems, avoiding customer lock-in and offering a single, compatible, distributed computing workload solution throughout the enterprise."
Platform Computing offers a distributed analytics platform that is fully compatible with the Apache Hadoop MapReduce programming model and allows current MapReduce applications to easily move to Platform's distributed computing workload platform while also supporting multiple distributed file systems.
Platform Computing's solution also provides enterprise-class capabilities to deliver scaled-out MapReduce workload distribution. Designed to support more than 1,000 simultaneous applications, organizations can dramatically increase server utilization for up to 40,000 cores across all resources resulting in a high return on investment, Platform officials said. And unlike other solutions that lack multiple analytic application support and scalable distributed workload engines, Platform's distributed workload services are designed for high scalability, fast performance, and extreme application compatibility through its low-latency distributed architecture, the company said. MapReduce application workloads can now run with high reliability under powerful central management, thereby meeting IT services level agreements (SLAs) with high reliability and consistency.