.svg){kind=logo}
Hazelcast is an in-memory data processing platform that provides distributed data structures and computing capabilities. It's used by organizations that need ultra-low latency data access, real-time analytics, and distributed computing for demanding applications.
Companies adopt Hazelcast to achieve high-performance data processing, build real-time applications, and process data at scale with minimal latency.
Hazelcast creates a distributed cluster where data is stored in-memory across multiple nodes. Applications access data through familiar data structures like maps, queues, and lists that are automatically distributed and replicated across the cluster. This approach provides both performance and resilience.
The platform supports distributed computing through task execution, allowing applications to send computation to where data lives, reducing data movement and improving performance. Hazelcast also provides streaming capabilities for processing continuous data flows.
The architecture emphasizes high availability, with automatic failover and data replication ensuring the system remains operational even if nodes fail.
Distributed Data Structures: Maps, queues, sets, lists, and other structures that work across a cluster.
In-Memory Computing: Process data in-memory for ultra-low latency access and computation.
Distributed Computing: Execute tasks across cluster nodes for parallel processing.
Stream Processing: Process continuous data flows with stateful transformations and aggregations.
High Availability: Automatic failover and data replication for resilient systems.
Real-Time Analytics: Organizations process data at low latency for real-time insights and decisions.
Caching Layer: Applications use Hazelcast as a distributed cache to reduce database load.
Session Management: Web applications store session data in Hazelcast for low-latency access and resilience.
Financial Systems: Trading platforms and financial systems require ultra-low latency for competitive advantage.
Begin with distributed data structures and basic cluster setup, develop expertise in streaming and real-time analytics, and advance to cluster optimization, deployment patterns, and complex distributed computing scenarios.
