Power your cloud-native applications with the world’s leading open-source in-memory data platform.
These guides demonstrate how to get started quickly with Hazelcast IMDG and Hazelcast Jet.
Learn how to store and retrieve data from a distributed key-value store using Hazelcast IMDG. In this guide you’ll learn how to:
Learn how to build a distributed data processing pipeline in Java using Hazelcast Jet. In this guide you’ll learn how to:
Hazelcast platform is a distributed, highly available, cloud-native, in-memory data platform with unified user experience and management tooling. It is massively parallel and can process millions of operations per second with latencies in microseconds at scale. It is simple to use, no additional or third party coordination processes are required.
Power your cloud-native applications with the world’s leading open-source in-memory data platform.
Hazelcast brings distributed data structures, processing, and coordination services in a light-weight package. Hazelcast services are carefully integrated so that you can compose them into powerful distributed applications.
Hazelcast can process millions of records per second, store Terabytes of data in-memory, and handle thousands of clients whilst maintaining microsecond access times. Hazelcast regularly proves itself to be a faster and more powerful option than NoSQL stores such as Redis, Cassandra or MongoDB.
Hazelcast clusters can add capacity and CPU by starting more member processes. The members can be added whilst the cluster is still running resulting in zero downtime. Data and computations are automatically rebalanced by the cluster to make use of all resources available.
Hazelcast provides safety by replicating data and computations across the cluster. Every member of the cluster is responsible for a portion of primary and replica entries. There is no concept of masters or replica processes. Hazelcast can intelligently place replicas on the safest member, on another physical machine or even another rack.
Extend Hazelcast with a rich set of plugins. Connect Hazelcast to a variety of systems including all major databases, storages and messaging systems [see all connectors]. Use the deployment kits for simple deployment to any cloud or container [see Cloud Discovery connectors]. Framework accelerators simplify using Hazelcast from Spring, Hibernate, web container or as a cache provider.
The Management Center enables monitoring and management of the Hazelcast cluster. This includes monitoring the overall state of clusters, resource utilization, running workloads as well as detailed analysis and browsing of data structures in real time, updating map configurations, and taking thread dumps from nodes.
Hazelcast combines distributed data structures, distributed data processing, distributed coordination, elasticity, connectors and integration with Spring and Hibernate. These capabilities bring several benefits to enterprise deployments, including the ability to handle thousands of operations per second, prevent the loss of data after crashes, and dynamically scale as new servers are added.
A data stream is a series of isolated records. Make it queryable using Hazelcast. Cache recent values, correlate simple events with complex events, join streams or combine (aggregate) multiple values to build and maintain a queryable view of the streaming data. This reduces data access time for consumers and allows event-driven behavior.
Use Hazelcast to speed up your MapReduce, Spark, or custom Java data processing jobs. Load various data sets to a cluster cache and perform fast compute jobs on top of the cached data. You get significant performance gains by an combining in-memory approach with co-location of job and data and with parallel execution.
Hazelcast is one of the most popular open-source caching solutions, ensuring that data is in the right place when it’s needed for optimal performance.
Hazelcast is often used as an operational memory layer for databases in order to improve the performance of applications, to distribute data across servers, clusters and geographies, to ingest data at very high rates, and to manage large data sets.
Hazelcast has a broadcast messaging system offering a comparable set of features to JMS topics. Use Hazelcast messaging to glue Hazelcast services to powerful applications.
Hazelcast can function as an in-memory NoSQL key-value store. Increasingly, more and more deployments are seeing the advantages of ever-expanding RAM sizes at lower costs.
Package the embeddable and lightweight Hazelcast library and your application into a single container (such as Docker container or a Java JAR) to create self-contained data processing microservices.
The community has built up a large collection of resources that will allow newcomers to quickly harness the power of Hazelcast.
See how the distributed compute features of Hazelcast can be used to build a rule engine for low-latency, high-throughput transaction processing.
Machine learning (ML) brings exciting new opportunities, but applying the technology in production workloads has been cumbersome, time consuming, and error prone. In parallel, data generation patterns have evolved, generating streams of discrete events that require high-speed processing at extremely low response latencies. Enabling these capabilities requires a scalable application of high-performance stream processing, distributed application of ML technology, and dynamically scalable hardware resources.
This video by Hazelcast senior solutions architect Sharath Sahadevan walks through a setup of WAN Replication on Google Cloud Platform.
Whether you're interested in learning the basics of in-memory systems, or you're looking for advanced, real-world production examples and best practices, we've got you covered.