In-Memory Data Grid for Low Latency Apps VMware GemFire
Scale your data services on demand to support high-performance, real-time apps with an ultra-high speed, in-memory data and compute grid.
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Scale your data services on demand to support high-performance, real-time apps with an ultra-high speed, in-memory data and compute grid.
Perform read and write operations in an instant. Get parallel message queues, continuous availability and an event-driven architecture you can scale dynamically, with no downtime.
Traditional databases are often too brittle or unreliable for use with microservices. With VMware GemFire, apps get low-latency responses to data access requests and always return fresh data.
Ensure resiliency on-premises or in the cloud. Maintain transaction integrity across distributed nodes. Support high-concurrency, low-latency data operations.
Get a high-performance application cache that’s available across platforms, including the VMware Tanzu platform, major hyperscalers and Red Hat OpenShift.
Discover how VMware GemFire supports critical applications in some of the world's largest organizations.
Easily scale to meet demanding usage peaks. Support scale-out patterns with dynamically rebalanced data partitioning. Elasticity reduces steady-state runtime costs.
Make it easy to add a cache to your .NET or Node.js applications. Enjoy frictionless access to the full capabilities of VMware GemFire.
Apps can subscribe to real-time events and react immediately. Continuous queries notify the app about new data, reducing overhead on the SQL database.
Ensure security with authentication and authorization using OAuth 2 standard, which supports Transport Layer Security so all dataflows are encrypted.
Even during huge peaks in concurrent access, your users can count on predictable low latency because of the in-memory, horizontally scalable architecture.
Use the Multi Geo replication capability for active-active configuration. For disaster recovery, deploy VMware GemFire across sites in an active-passive setup.
These pages call many databases for the necessary data, much of which is static and can be precomputed overnight and stored in the GemFire cache. Some data is slowly changing, making it ideal for caching.
Customers need accessible shopping carts no matter the app server. A traditional, nonscalable database may not keep up. VMware GemFire easily scales out for millions of concurrent accesses per second.
Some backing stores can’t keep up with high-volume transaction processing. Gain an in-memory data grid that supports high data consistency, providing real-time responses at scale.
Many heritage data sources are deployed adjacent to modern runtimes. Use VMware GemFire as an inline cache or cache aside to make the data available to a new generation of microservices.
The data grid can manage terabytes of data in-memory and push notifications for mission-critical processes. Systems can immediately access incoming data and take action.
These pages call many databases for the necessary data, much of which is static and can be precomputed overnight and stored in the GemFire cache. Some data is slowly changing, making it ideal for caching.
Customers need accessible shopping carts no matter the app server. A traditional, nonscalable database may not keep up. VMware GemFire easily scales out for millions of concurrent accesses per second.
Some backing stores can’t keep up with high-volume transaction processing. Gain an in-memory data grid that supports high data consistency, providing real-time responses at scale.
Many heritage data sources are deployed adjacent to modern runtimes. Use VMware GemFire as an inline cache or cache aside to make the data available to a new generation of microservices.
The data grid can manage terabytes of data in-memory and push notifications for mission-critical processes. Systems can immediately access incoming data and take action.
Visit the GemFire Developer Center for videos, code samples and guides.
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Get started with VMware GemFire and enjoy a high-performance application cache that offers consistent scaling across data centers.