Get Started

Get Started

These guides demonstrate how to get started quickly with Hazelcast IMDG and Hazelcast Jet.

Hazelcast IMDG

Learn how to store and retrieve data from a distributed key-value store using Hazelcast IMDG. In this guide you’ll learn how to:

  • Create a cluster of 3 members.
  • Start Hazelcast Management Center
  • Add data to the cluster using a sample client in the language of your choice
  • Add and remove some cluster members to demonstrate data balancing capabilities of Hazelcast

Hazelcast Jet

Learn how to build a distributed data processing pipeline in Java using Hazelcast Jet. In this guide you’ll learn how to:

  • Install Hazelcast Jet and form a cluster on your computer
  • Build a simple pipeline that receives a stream of data, does some calculations and outputs some results
  • Submit the pipeline as a job to the cluster and observe the results
  • Scale the cluster up and down while the job is still running

Resources

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Hazelcast for Healthcare

White Paper

This whitepaper discusses how an in-memory computing platform is used in the healthcare industry to help improve patient care.

Hazelcast with Kubernetes

White Paper

Learn how the cloud-native architecture of Hazelcast works with Kubernetes when deploying fast cloud applications.

Credit Value Adjustment Calculation Reference Implementation

White Paper

This paper covers implementation details behind the Hazelcast credit value adjustment risk calculation solution.

Credit Value Adjustment Calculation Reference Architecture

White Paper

This paper describes a modern architecture for calculating risk so banks can rethink their legacy systems and aspire for greater efficiency.

Hazelcast WAN Replication on Google Cloud Platform

Video
| Video

This video by Hazelcast senior solutions architect Sharath Sahadevan walks through a setup of WAN Replication on Google Cloud Platform.

Applied Machine Learning in Real-Time with Distributed, Scalable, In-Memory Technology

Webinar
| Video
| 60 minutes

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.

Cloud-Native Scalable Rule Engine Demo

Video
| Video

See how the distributed compute features of Hazelcast can be used to build a rule engine for low-latency, high-throughput transaction processing.

Turbo-Charge Your Applications from z/OS to Edge to Multi-Cloud with In-Memory Computing

Webinar
| Video
| 60 minutes

Mainframe computers are used at many companies today, but the need for more cost-effectiveness is forcing changes. A popular strategy, mainframe optimization, balances mainframe usage with in-memory computing closer to the application tier, reducing unnecessary MIPS. At the same time, it adds powerful new architectures related to cloud, microservices, and data streaming. An integration with […]

Hazelcast In-Memory Computing Platform (IMCP) and Red Hat Data Grid (RHDG) Comparison

White Paper

Read why you should use Hazelcast over the Red Hat Data Grid for your application acceleration and architecture modernization initiatives.

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