Best In-Memory Databases for Dragonfly
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Compare the Top In-Memory Databases that integrate with Dragonfly as of July 2025
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This a list of In-Memory Databases that integrate with Dragonfly. Use the filters on the left to add additional filters for products that have integrations with Dragonfly. View the products that work with Dragonfly in the table below.
What are In-Memory Databases for Dragonfly?
In-memory databases store data directly in a system’s main memory (RAM) rather than on traditional disk-based storage, enabling much faster data access and processing. This approach significantly reduces latency and increases performance, making in-memory databases ideal for real-time analytics, high-frequency transactions, and applications requiring rapid data retrieval. They are often used in industries like finance, telecommunications, and e-commerce, where speed and scalability are critical. In-memory databases support both SQL and NoSQL models and typically include features for data persistence to avoid data loss during system shutdowns. Ultimately, they provide high-speed performance for time-sensitive applications while ensuring data availability and integrity. Compare and read user reviews of the best In-Memory Databases for Dragonfly currently available using the table below. This list is updated regularly.
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Redis
Redis Labs
Redis Labs: home of Redis. Redis Enterprise is the best version of Redis. Go beyond cache; try Redis Enterprise free in the cloud using NoSQL & data caching with the world’s fastest in-memory database. Run Redis at scale, enterprise grade resiliency, massive scalability, ease of management, and operational simplicity. DevOps love Redis in the Cloud. Developers can access enhanced data structures, a variety of modules, and rapid innovation with faster time to market. CIOs love the confidence of working with 99.999% uptime best in class security and expert support from the creators of Redis. Implement relational databases, active-active, geo-distribution, built in conflict distribution for simple and complex data types, & reads/writes in multiple geo regions to the same data set. Redis Enterprise offers flexible deployment options, cloud on-prem, & hybrid. Redis Labs: home of Redis. Redis JSON, Redis Java, Python Redis, Redis on Kubernetes & Redis gui best practices.Starting Price: Free -
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memcached
memcached
You can think of it as a short-term memory for your applications. memcached allows you to take memory from parts of your system where you have more than you need and make it accessible to areas where you have less than you need. The first scenario illustrates the classic deployment strategy, however you'll find that it's both wasteful in the sense that the total cache size is a fraction of the actual capacity of your web farm, but also in the amount of effort required to keep the cache consistent across all of those nodes. With memcached, you can see that all of the servers are looking into the same virtual pool of memory. Also, as the demand for your application grows to the point where you need to have more servers, it generally also grows in terms of the data that must be regularly accessed. A deployment strategy where these two aspects of your system scale together just makes sense.
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