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

Machine Learning Inference at Scale with Python and Stream Processing


There is frequently an “impedance mismatch” between developing and training a machine learning model (a data scientist’s job) and then deploying that model to perform at scale in a production environment (a data engineer’s job). How do you make a trained prediction model usable in real time, while the user is interacting with your software? What does it take to go from fast trial-and-error runs on historical data to models that perform at production scale, in real time?

In this talk we will show you how to write a low-latency, high throughput distributed stream processing pipeline (in Java), using a model developed in Python.

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