Hazelcast Platform

The Real-Time Intelligent Applications Platform

Fast Cloud Applications

Large-scale computations need integration of transactional, operational, and historical data into a single platform. Connecting all the relevant data sources is hard enough, but procuring the compute power and storage space adds another level of complexity. You need these compute-intensive jobs to always complete within stringent SLAs, so you need high speed and efficient use of hardware resources. And you only want the resources you need, only when you need them.

Hazelcast provides the foundation to develop and deploy fast, highly scalable applications for you to run large-scale calculations, simulations, and other data- and compute-intensive workloads.

  • Rapidly develop and deploy distributed applications using familiar programming patterns in SDKs for Java, C#, Go, Node.js, Python and C++. Leverage Hazelcast user-defined functions to reuse existing code into a modern, cloud-native architecture.
  • Run zero-downtime jobs to ensure completion within SLAs, via elastic clustering, high-availability and multi-DC, dynamic patching, and app redeployment.
  • Gain extremely high performance in a cloud-native architecture running as self-managed (bare metal, Kubernetes, Red Hat OpenShift), as a managed service on AWS, Azure, GCP, and at the edge.

Real-Time Decision-Making

Real-time decision-making entails immediately analyzing a comprehensive set of information to take the best action, often without any human intervention. But overwhelming complexity arises from disparate data silos plus the many moving parts that need to be bolted together in a real-time system. You end up struggling with low agility, putting your operations at a disadvantage. And you know it does not help to simply add more of the same components to your infrastructure.

Hazelcast enables optimal real-time decisions thanks to a low-latency, distributed memory architecture for data and computations.

  • Integrate live data streams of any scale including remote applications, devices, and message brokers such as Apache Kafka, Apache Pulsar, AWS Kinesis, and RabbitMQ, with historical and operational data to add greater context for decisions.
  • Efficiently run thousands of queries over billions of live events.
  • Store indexed data in-memory as a fast, drill-down analytics store.

Accelerated Transaction Processing

Large-scale computations need integration of transactional, operational, and historical data into a single platform. Connecting all the relevant data sources is hard enough, but procuring the compute power and storage space adds another level of complexity. You need these compute-intensive jobs to always complete within stringent SLAs, so you need high speed and efficient use of hardware resources. And you only want the resources you need, only when you need them.

Hazelcast provides the foundation to develop and deploy fast, highly scalable applications for you to run large-scale calculations, simulations, and other data- and compute-intensive workloads.

  • Rapidly develop and deploy distributed applications using familiar programming patterns in SDKs for Java, C#, Go, Node.js, Python and C++. Leverage Hazelcast user-defined functions to reuse existing code into a modern, cloud-native architecture.
  • Run zero-downtime jobs to ensure completion within SLAs, via elastic clustering, high-availability and multi-DC, dynamic patching, and app redeployment.
  • Gain extremely high performance in a cloud-native architecture running as self-managed (bare metal, Kubernetes, Red Hat OpenShift), as a managed service on AWS, Azure, GCP, and at the edge.