Zing and JBoss Data Grid deliver consistent performance for best-in-class in-memory solutions
Azul Systems®, Inc. (Azul), the award-winning leader in Java runtime scalability, announced today that Azul’s Zing runtime for Java is now certified and fully supported with Red Hat JBoss Data Grid. Ideal for JBoss Data Grid deployments leveraging larger data nodes in transactional Big Data implementations, Azul’s Zing allows organizations to accelerate time to market, improve application runtime consistency and deliver higher sustained throughput for their JBoss Data Grid-based implementations.
By deploying Red Hat JBoss Data Grid and Azul’s Zing together in business-critical use cases, organizations can scale up (into servers with larger memory configurations) or out as required, without sacrificing performance. JBoss Data Grid provides fast, in-memory data access and elastic scale out, while Zing eliminates all JVM-based performance hiccups, drastically reducing peak latencies and providing unprecedented response time consistency. The combined solution is especially attractive for organizations looking to scale up memory heap sizes or store large in-memory datasets on fewer nodes while delivering predictable application performance.
Azul’s Zing is designed for enterprise Java applications and workloads that require any combination of large memory, high transaction rates, low latency, consistent human-scale response times or high sustained throughput. Zing provides the following benefits for enterprises implementing JBoss Data Grid:
"Zing is ideal for Red Hat JBoss Data Grid customers who are looking to scale up memory heap sizes and store large datasets on fewer nodes while still achieving very predictable response time performance," noted Mike Piech, general manager, Middleware, Red Hat.
"By combining Red Hat JBoss Data Grid with Azul’s Zing, Java-based businesses realize a significant competitive advantage,” said Scott Sellers, president and CEO of Azul Systems. “Now enterprises can deploy business-critical Java applications requiring large in-memory data sets, consistent real-time performance and scalability in a cost-effective and operational friendly manner.”