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

A Reference Guide to Stream Processing

Guide

The goal of streaming systems is to process big data volumes and provide useful insights into the data prior to saving it to long-term storage. The traditional approach to processing data at scale is batching; the premise of which is that all the data is available in the system of record before the processing starts. In the case of failures, the whole job can be simply restarted.

While quite simple and robust, the batching approach clearly introduces a large latency between gathering the data and being ready to act upon it. The goal of stream processing is to overcome this latency. It processes the live, raw data immediately as it arrives and meets the challenges of incremental processing, scalability and fault tolerance.

This white paper introduces you to the domain of stream processing covering these topics:

  • Use cases that benefit from stream processing
  • Building blocks of a stream processing solution
  • Key concepts used when building a streaming pipeline: definition of the dataflow, keyed aggregation, windowing
  • Runtime aspects and tradeoffs between performance and correctness
  • Overview of distributed stream processing engines
  • Hands-on examples based on Hazelcast Jet®

Who Should Read It?

This paper is intended for software architects and developers who are planning or building system utilizing stream processing, fast batch processing, data processing microservices or distributed java.util.stream.

What’s In This White Paper?

  • Fast Processing of Infinite and Big Data
  • What is Stream Processing
  • When to Use Stream Processing
  • The Building Blocks of Stream Processing
  • Transformations
  • Windowing
  • Running Jobs
  • Fault Tolerance
  • Sources and Sinks
  • Overview of Stream Processing Platforms
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