Pivotal, a company at the intersection of big data, PaaS, and agile development, is bringing an in-memory transactional store to Hadoop with Pivotal GemFire XD that is tightly integrated with Pivotal HD, a commercially supported distribution of the Apache Hadoop stack.
Pivotal HD 1.1 with GemFire XD provides a read-write store allowing customers to build real-time OLTP applications at cloud scale on top of Hadoop. In Pivotal HD Enterprise, Pivotal GemFire XD in-memory data service combines with the Pivotal HAWQ query engine, which adds SQL’s expressive power to Hadoop to provide the industry’s first production quality platform for creating closed loop analytics solutions that combine OLTP and OLAP, using Hadoop as the common storage substrate. The Pivotal GemFire XD product brings to Hadoop the enterprise-tested GemFire technology that powers some of the most mission critical data-driven applications available, including financial services, federal government, logistics and eCommerce portals.
Businesses today need to take in a huge amount of data from varied sources, process it immediately, and use it to make critical business decisions. Failing to act on data in real-time can lead to lost sales, oversupply of inventory, and missed windows of opportunity that can threaten the very survival of the business. This trend is even more pronounced as the Hadoop Distributed File System (HDFS) becomes the data substrate for the next generation of data infrastructures. Already a set of integrated, enterprise-scale services are evolving on top of HDFS – stream ingestion, analytical processing, and transactional serving. Pivotal GemFire XD marries a SQL in-memory data grid with Hadoop to offer high scale and makes it suitable for operational environments. When combined with Pivotal HD and HAWQ technology, enterprises can act and react in ways not achievable with traditional or existing solutions.
By combining the real day-to-day events of a business with the analytics systems that monitor business behavior, customers can act prescriptively to events while they are happening, or even predict events before they happen.
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