Struggling to choose between Redis and Hazelcast? Both products offer unique advantages, making it a tough decision.
Redis is a Development solution with tags like caching, inmemory, keyvalue-store.
It boasts features such as In-memory data structure store, Supports various data structures (strings, hashes, lists, sets, sorted sets, bitmaps, hyperloglogs, geospatial indexes, streams), Used as a database, cache, and message broker, Provides high performance and low latency, Supports replication, clustering, and high availability, Supports a wide range of programming languages, Provides a rich set of commands and APIs, Supports data persistence (RDB and AOF) and pros including High performance and low latency, Flexible and versatile data structures, Supports a wide range of use cases, Easy to set up and configure, Scalable and highly available, Open-source and free to use.
On the other hand, Hazelcast is a Development product tagged with caching, processing-streams, clustering.
Its standout features include Distributed in-memory data store, Low latency data access, Automatic sharding and rebalancing, ACID transactions, Querying and aggregation, Event journaling, Multi-datacenter replication, Web session clustering, Continuous query, Machine learning, and it shines with pros like Fast performance, Easy scalability, High availability, Flexible deployment options, Open source, Rich ecosystem.
To help you make an informed decision, we've compiled a comprehensive comparison of these two products, delving into their features, pros, cons, pricing, and more. Get ready to explore the nuances that set them apart and determine which one is the perfect fit for your requirements.
Redis is an open-source, in-memory data structure store, used as a database, cache and message broker. It supports data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes and streams.
Hazelcast is an open source in-memory data grid that enables distribution of data and computation across servers for scalability, speed, and resilience. It is commonly used for caching, processing streams, and clustering.