MILPITAS, Calif., May 19, 2017 /PRNewswire/ -- Esgyn Corporation, the leader in Converged Big Data solutions that empower global enterprises to realize the potential of Big Data, has partnered with Ampool to deliver a product that combines the power of EsgynDB with Ampool's Active Data Store (ADS).
Enterprises aspire to provide personalized real-time customer experiences, yet have a bewildering array of technologies to choose from. With rapid expansion of mobile devices, social media, and adoption of remote sensors and devices, the volume, velocity, and variety of transactions have exploded as well. Disk-based architectures struggle to deliver real time performance. With the cost of memory plummeting, in-memory storage offers a realistic cost versus performance trade-off for Big Data workloads. For performance reasons, many enterprises are planning, implementing, or expanding the use of in-memory data platforms.
EsgynDB, a massively parallel query and transaction engine with a sophisticated optimizer, parallel data flow engine, and rock-solid Distributed Transaction Manager, aims to harness the power of in-memory technology by tight integration with Ampool's ADS. Ampool's ADS pools chip-based memory (volatile and non-volatile) across the cluster for parallel, high-speed data storage and access, offering in-memory performance and simplicity to real-time data-centric applications across diverse data platforms.
"By supporting full in-memory execution and leveraging the pooled memory resources across the same data platform, the combined product will deliver up to 5-times better performance in processing transactions and reporting workloads," said Hong Ding, CEO of Esgyn, "This is achieved all while retaining full concurrency and scalability capabilities. This in-memory capability will provide our customers with on-demand flexibility to maximize the utilization of memory resources for sub-second sensitive workloads, while minimizing any data movement, to meet customers' real-time data processing and analytics demands," said Ding.
Key benefits of the combined product are:
Real-time, In-memory performance: Ampool's distributed in-memory layer complements Esgyn's massively parallel SQL query and transaction engine to enable analytical processing of hot (active) data as events are happening within the enterprise. By using volatile and persistent tables to process live and historical data, enterprises can unify transactions, operational reporting, and analytics onto one Big Data platform.
Enterprise-ready: Both Esgyn and Ampool are built on open-cores (Apache Trafodion and Apache Geode respectively) with more than 15 years of history in enterprise production environments (HP Nonstop SQL/MX and Pivotal Gemfire). Customers can expect a worry-free, highly reliable and available platform.
Flexible: The solution supports a wide-range of client tools and applications using standard database and Hadoop interfaces, including JDBC, ODBC, Hibernate and Kafka.
"We are excited to partner with Esgyn," said Milind Bhandarkar, CEO of Ampool, "The benefits of the combined product are compelling. You have two teams and technologies steeped in enterprise experience coming together to deliver an enterprise-ready platform that combines the best of Esgyn's powerful SQL engine with Ampool's in-memory storage engine providing unparalleled speed at scale."
The combined product will be available later this year. Interested customers can learn more by contacting marketing@esgyn.com.
About Esgyn Corporation
Esgyn™ is the leader in Converged Big Data solutions that empower global enterprises to realize the potential of Big Data. With the industry's first All-in-One SQL Database for Big Data, Esgyn is leading the way enterprises cope with ever increasing data management needs on-premises and in the cloud.
About Ampool
Ampool's mission is to enable real-time applications and reduce time to actionable insights through managing and serving active data via a robust, scale-out, in-memory platform. Ampool is aiming to democratize in-memory analytical processing, unifying the entire data analytics platform.
