IN-MEMORY DATA GRID

High performance, elastic scalability, always available

Great user experience is ever-more dependent on application performance and quality. Even a few seconds delay can mean the difference between success and failure for a new business initiative. To capitalize on customer engagement, you need to know your customers and provide targeted offers that prompt them to interact in real time. Data bottlenecks are becoming more common as organizations need to process larger volumes, greater varieties, and a higher velocity of data to meet customer expectations and deliver personalized data-driven engagement.

With Red Hat® JBoss® Data Grid—an in-memory, distributed, NoSQL datastore solution—your applications can access, process, and analyze data at in-memory speed to deliver a superior user experience.

ADOPTION DRIVERS

Modern technology trends drive adoption of in-memory data grids.

Database and transaction caching; transient data store

Data caching and transient data storage are the most common data grid use cases. Data grids, such as JBoss Data Grid, are deployed as a speedy in-memory datastore for the most frequently accessed data. A variation on caching is the use of data grids to store transient data (e.g., web sessions and shopping cart data, common in e-commerce applications).

You gain improved performance and scalability of the data grid-enabled applications and reduced access to expensive database management systems and transactional back ends. This often implies a reduction in the cost of running these systems.

Primary NoSQL database

Red Hat JBoss Data Grid is an in-memory, distributed, NoSQL key-value datastore that offers a simple, flexible way to store a variety of data without the constraints of a fixed data model. It can also be used as document store with the support for schema. JBoss Data Grid provides a flexible persistence model for backup, recovery, and post-processing reasons.

In addition to exploiting fast access to in-memory data, applications can take advantage of advanced functionality to perform parallel distributed workload execution, run rich queries, manage transactions, and elastically scale and recover from network or system faults.

Low-latency compute grid

Data grids bring data physically closer to data processing to reduce latency and increase application performance. JBoss Data Grid enables a scale-out architecture that deploys application logic next to the data kept in memory in each node. This is preferred over sending large sets of data to the compute nodes over the wire. This approach helps reduce network traffic significantly and increase application performance dramatically. Event-driven computing is supported by triggering application logic as data changes in the cluster.

Examples of real-time compute and analytics include fraud detection and risk management applications.

Elastically scalable, in-memory data accelerator

Data grids are well-suited to handle big data's "big 3 Vs." First, they support the velocity needs of big data. That is, data grids support hundreds of thousands of in-memory data updates per second. Second, like NoSQL datastores, data grids support big data variability. Finally, data grids can be clustered and scaled to support large volumes of data.

Similarly, the "things" in IoT all generate great volumes of data, often at more frequent intervals. JBoss Data Grid enables the storage of tens of terabytes of data, faster response times, and almost-instant analytics. This makes it feasible to process IoT data at nearly the same speed that it's generated.

CERTIFIED INTEGRATION

Use together with Red Hat JBoss Middleware

Protect your sensitive data

To support strict security requirements, JBoss Data Grid provides secured communications between client and server—and between server nodes in a secure cluster. Authentication, role-based authorization, and access control is integrated with existing security and identity structures.

With JBoss Data Grid, only trusted users, services, and applications get access.