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.
“Platform Computing has been providing solutions for distributed computing infrastructures that align well to the MapReduce paradigm,” said Carl Olofson, research vice president at IDC, in a statement. “Analysis of unstructured data provides a competitive advantage to companies looking to understand behaviors and trends. Dynamically defined data can require very rapid analysis in bulk, and sensor data has volumes that swamp conventional data centers. Customers need a robust solution to manage and process their dynamically defined data, their sensor data and their unstructured data. MapReduce has proven to be a leading tool for analyzing this data, but customers need enterprise-class solutions to ensure manageability and scalability for these environments. Platform is positioned well to provide distributed workload and enterprise class middleware to address these challenges.”
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.”
By extending enterprise-class capabilities to MapReduce distributed workloads, customers benefit from the ability to scale to thousands of commodity server cores for shared applications. The results include the ability to perform at very high execution rates, offer IT manageability and monitoring while controlling workload policies for multiple lines of business users and applications and obtain built-in, high availability services that ensure quality of service, Platform officials said.
“MapReduce is an important technique for handling big data problems, said Paul Kent, vice president of Platform research and development at SAS Institute Inc. “SAS is looking forward to continuing our enterprise-class partnership with Platform Computing as we integrate this technique into our Data Management and Business Analytics software.”
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.