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

About the Author

Nicolas Frankel

Nicolas Frankel

Developer Advocate, Hazelcast

Nicolas Fränkel is a Developer Advocate with 15+ years experience consulting for many different customers, in a wide range of contexts (such as telecoms, banking, insurances, large retail and public sector). Usually working on Java/Java EE and Spring technologies, but with focused interests like Rich Internet Applications, Testing, CI/CD and DevOps. Currently working for Hazelcast. Also double as a teacher in universities and higher education schools, a trainer and triples as a book author.

Follow me on
Loading

No posts were found matching that criteria.

Designing an Evergreen Cache with Change Data Capture

Nicolas Frankel
by Nicolas Frankel

It has been said that there are two things hard in software development, naming things and cache invalidation (while some add off-by-one errors to the mix). I believe that keeping the cache in sync with the source of truth might count as a third one. In this post, I’d like to tackle this issue, describe […]

An Experiment in Streaming: Bytecode Continuous Deployment

Nicolas Frankel
by Nicolas Frankel

Once one starts their journey in data streaming, one starts to discover a lot of applications beyond just the standard Extract-Transform-Load pattern. The traditional model to deliver a new version of a Java application is to stop the process, deploy the new JAR/WAR, and start the process again. This directly results in downtime: in this […]

An Easy Performance Improvement with EntryProcessor

Nicolas Frankel
by Nicolas Frankel

A lot of a developer’s work is about transforming and aggregating data: Increasing the quantity of a product in a shopping cart Applying VAT on the price of a product Computing the price of a shopping cart Etc… Sometimes, one needs the features of a full-fledged stream processing engine, such as Hazelcast Jet, sometimes not. […]

Persisting In-Memory Data for Later Usage

Nicolas Frankel
by Nicolas Frankel

Among the many capabilities of an in-memory data grid (IMDG), caching is one of the most well-known and used. However, as its name implies, data resides in memory. The memory is of finite capacity. In order not to put more data than memory can handle, we must decide how to curate it. Hazelcast comes with […]

Open Gitter Chat