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 Computing has been providing solutions for distributed computing infrastructures that align well to the MapReduce paradigm", stated Carl Olofson, Research Vice President, IDC. "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 centres. 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."
"MapReduce is an important technique for handling big data problems", stated Paul Kent, Vice President Platform R&D at Cary, NC-based SAS. "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."
"Many of Platform's customers already use our products to run complex analytics and other distributed workload services", stated Ken Hertzler, Vice President, Product Management, Platform Computing. "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."
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. Today, companies need analytics that can perform at the speed of business in order to make the best business decisions possible.
For more than 18 years, Platform Computing's history has been rooted in providing distributed computing and workload management solutions to leading enterprise companies and organizations. Platform's core distributed workload engines, found in Platform LSF and Platform Symphony, easily lend themselves to the issue of handling "big" data because they provide the support necessary to access, process and analyze multiple data types efficiently and quickly at large volume and to enterprise-class standards.
Platform Computing offers a distributed analytics platform that is fully compatible with the Apache Hadoop MapReduce programming model. This 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, organisations can dramatically increase server utilization for up to 40,000 cores across all resources resulting in a high return on investment. Unlike less sophisticated 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. MapReduce application workloads can now run with high reliability under powerful central management, thereby meeting IT SLAs with high reliability and consistency.