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An easy decision for business-critical financial services applications, Redis has three times the performance and one-third the latency of Hazelcast.
Business-critical enterprise applications need speed and reliability, especially in critical industries such as financial services. Building the infrastructure for that digital economy calls for ultra-fast, real-time data processing.
Redis Enterprise, built on top of Redis open source, delivers on that promise. Redis Enterprise is a scalable, in-memory, multi-model data platform that keeps pace with everything an organization requires. With brag-worthy performance, sub-millisecond latency, high availability, and impressive extensibility, Redis comes in at a lower cost than In-Memory Data Grids (IMDGs).
Redis is one of the most popular databases available. DB-Engines ranks Redis as #6 in popularity overall, and in second place for NoSQL databases. Popularity isn’t simply a matter of “They like us!” (although, you know, they do). It suggests that it’s easier to find developers and IT staff who know the platform. (Hazelcast is at #48 on the DB-Engine rankings).
If you’re looking for a solution that is more than just an in-memory compute and cache platform designed for Java applications, then Redis Enterprise is the better choice.
How does Redis Enterprise compare with Hazelcast Enterprise?
At first glance, Redis and Hazelcast might look similar. Both are built on open source projects. Both aim to speed up application performance by employing in-memory technologies originally focused on caching. When you look more closely, however, the architectures, design principles, and functionalities are radically different.
Redis Enterprise is a real-time data platform written in C that can be deployed anywhere: on any infrastructure, cloud platform, or operating system. It is a cache and a multi-model database that enables developers to address a wide range of use cases, such as search, JSON, and time series. Redis’ strengths include: global linear scalability, sub-millisecond latency, 99.999% availability, simplified administration, and native Kubernetes deployment.
| Hazelcast Enterprise | Redis Enterprise | ||
|---|---|---|---|
| Higher throughput at lower latency | • | ||
| Designed for performance and scalability | Development language | Java | C |
| Optimized architecture | • | • | |
| On-demand scalability | • | ||
| Number of client libraries/SDKs supported | • >70 | ||
| Simplified developer and DevOps experience | Multi-use case extensibility | • | |
| Supports advanced Kubernetes operator | • | ||
| Vibrant community support | • | ||
| Enterprise-grade availability and resilience | High availability and disaster recovery | • | • |
| Automated Active-Active Geo Replication using conflict-free data types (CRDT) | • | ||
| Total cost of ownership | Intelligent enterprise tiered storage | • | |
| Optimized storage density | • | ||
| Deployment flexibility | Hybrid and multicloud support | • |
See how Redis Enterprise and Hazelcast Enterprise compare in more detail.
These companies chose Redis Enterprise over Hazelcast.
Redis Enterprise uses a shared-nothing cluster architecture. It is fault tolerant at all levels—with automated failover at the process level, for individual nodes, and even across infrastructure availability zones—as well as tunable persistence and disaster recovery.
Efficiently scaling database performance is critical for real-time applications. Redis Enterprise scales linearly and with zero downtime to provide resource-efficient databases that reliably deliver high throughput and sub-millisecond latency.
Redis Enterprise can be deployed anywhere: on any cloud platform, on-premises, or in a hybrid or multicloud architecture. It is also available on Kubernetes, Pivotal Kubernetes Service (PKS), and Red Hat OpenShift.
Redis Enterprise’s Active-Active database replication with conflict-free replicated data types (CRDTs) enables demanding applications to gracefully handle simultaneous updates from multiple geographic locations. That powers use cases such as fraud detection, rate limiting, and personalization on a global scale, without compromising latency or availability.
Redis modules that support data models, such as JSON, search, time series, graph, Bloom filters, and others can be readily applied to use cases such as fraud detection, personalization, transaction scoring, and product catalogs.
Cut costs with better resource utilization and intelligent storage tiering. Maintain performance by using expensive RAM only where you really need it.
