Get Started

Get Started

These guides demonstrate the operational flexibility and speed of the Hazelcast In-Memory Computing Platform. Set-up in seconds, data in microseconds. Operations and developer friendly.

Hazelcast IMDG

Find out for yourself how to get a Hazelcast IMDG cluster up and running. In this Getting Started guide you’ll learn how to:

  • Create a Cluster of 3 Members.
  • Start the 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 the automatic rebalancing of data and back-ups.

Hazelcast Jet

Learn how to run a distributed data stream processing pipeline in Java. In this Getting Started guide you’ll learn how to:

  • Start a Hazelcast Jet cluster in your JVM.
  • Build the Word Count application.
  • Execute the application wit Jet.
  • Push testing data to the cluster and explore results

As a next step, you will be able explore the code samples and extend your setup more features and connectors.

Jet Comparisons

Hazelcast Jet is highly performance and compares favorably against other popular data processing technologies.


Both Jet and Apache Spark™ are tools for distributed cluster computing. Both split processing jobs into parallel tasks that are distributed across the cluster. Data can be split and cached in the cluster for co-location leading to better performance.

A Spark cluster has many components and moving parts. Spark is designed for large, multi-tenant clusters.

Jet represents a lightweight approach. Jet has a low operational overhead so you can run more Jet clusters for isolation. Jet cluster can be deployed in a traditional client-server mode or it can be embedded and packaged with an application.


Traditionally, databases have been designed as durable systems of record. The data is stored to be queried later. Writes and queries are usually not mutually coordinated. The focus of traditional databases is querying rather than event-based low-latency workloads.

Hazelcast Jet is not a system of record and thus isn’t limited to the internal data set. Jet integrates data streams from external systems and continuously queries the streams. Jet keeps data temporarily, such as for open aggregations. However, one can use internal caches as an operational data store.

Jet queries are data-driven. New data trigger queries to refine results to be sent to consumers. So “writes” and queries are coordinated. Jet focuses on low-latency and real-time operations.

Java supports functional-style operations on streams of elements via the stream package. It is mainly designed as a convenience for Java developers to work with local collections.

Hazelcast Jet shifts this approach to a distributed world. It comes with a distributed implementation of the Collections API and contains the Pipeline API similar to to processes data in a distributed fashion.

Jet is much more powerful than the Stream API. Here are a few highlights:

  • Joining data
  • Working with infinite streams (windowing, event time processing)
  • Fault tolerance
  • Connectors to 3rd party systems

Serverless functions (Amazon Lambda or Azure Functions)

Serverless functions allow developers to take action by connecting to data sources or messaging solutions, thus making it easy to process and react to data events. Functions run in a managed environment so developers can focus on business logic, not infrastructure.

Hazelcast Jet is programmed using a similar functional, declarative style, to Apache Kafka®, Kinesis, JMS and Hazelcast IMDG, where the underlying platform invokes the functions based on the data events. Unlike serverless functions, Jet’s functions can be made stateful so that you can aggregate multiple data events or join data streams.

Jet is not a managed service as of now (but keep an eye on!). You have to deploy a Jet cluster and submit the functions, or jobs, to it. However, deploying Jet to the cloud is fairly straightforward.

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