Hazelcast IMDG is a clustered, in-memory data-grid that manages application data and distributes processing using in-memory storage and parallel execution for breakthrough application speed and scale. In this Quick Start Guide, learn what an in-memory data grid can be used for, how to do simple query operations with Hazelcast IMDG, what sharing means with Hazelcast, and more. This guide is intended for software architects and developers who are planning or building systems requiring distribute infrastructure for application scalability and performance.
This white paper, written by Java Champion Ben Evans, provides an introduction for architects and developers to Hazelcast®’s distributed computing technology.
Hazelcast Cloud is an enterprise-grade in-memory computing platform deployed and managed by the Hazelcast CloudOps team. The service
is powered by Hazelcast IMDG Enterprise HD and leverages widely adopted technologies, such as Docker and Kubernetes, to provide dynamic orchestration and containerization. Hazelcast Cloud supports applications developed in some of the most common languages, including Java, Node.js, Python. Go, and .NET.
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This is an example showing the use of YAML configuration, and populating a Hazelcast grid with data in JSON format that has been extracted from a traditional relational database. So the data goes from tables into “NoSQL” format. Hello YAML, Goodbye XML Hazelcast has many ways to be configured. Sensible defaults mean you don’t have […]
The CP Subsystem of Hazelcast IDMG 3.12 offers a new linearizable implementation of Hazelcast’s concurrency APIs on top of the Raft consensus algorithm. These implementations live alongside AP data structures in the same Hazelcast IMDG cluster (new BFFs, yay!). You can store large data sets on hundreds of Hazelcast members and coordinate your operations using […]
The past year has seen in-memory data grids (IMDG) continue to gain traction with the development community and large organisations alike. As you’ll see in the 2019 IMDG LinkedIn Survey results below, adoption of IMDG as a skill in LinkedIn profiles has risen by 43% YoY. Companies are turning to IMDGs as replacements for RDBMS […]
Knock-knock Your business is already under assault, whether you know it or not. It doesn’t matter whether you’re B2B or B2C; the same technology variables are driving pressure on your IT infrastructure. The growth of the global technology ecosystem has always been organic. While new technologies often layer on top of previous enablers (e.g. mobility […]
The data we continuously generate and use operates on an incredibly vast scale (think of Google, Amazon, Facebook, that level of data). Because of the breadth and depth of infrastructure required to stream incoming data and execute against it (and to avoid single points of failure), the ingestion and processing of data is distributed across […]
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Given the widely public reaction to the Commons Clause licensing change from another vendor, we felt it was important to let the community at large know that Hazelcast’s 10-year consistent stance on open-source licensing is not changing. In fact that was one of the first public questions posed by InfoQ to our new CEO, Kelly […]
Upcoming Webinar: What’s New in Hazelcast 3.11, November 6th Register Now! 1,100+ commits merged, 523 pull requests, 148 issues closed, 119 days passed, 41 committers contributed and an another Hazelcast release is out! And here are the updates: Hazelcast Works on Java 9/10/11 With Hazelcast IMDG 3.11, we are making sure all of our code […]
Piotr Szybicki recently published this blog post with the title, “Hazelcast on GCP (google cloud platform) part 1“. In this post, Piotr discusses the process of deploying and securing a Hazelcast IMDG cluster in GCP (Google Cloud Platform). In this article I am going to discuss the process of deploying, securing hazelcast cluster in to […]
We are happy to announce the release of Hazelcast Python Client 3.9! This new release brings the long-awaited Python 3 support to our client with bug fixes and performance improvements. Introduction As the number of packages that support Python 3 grows day by day, supporting Python 3 without dropping support for Python 2.7 was a […]
In this example. we’ll look at doing some analytics on an online shop to gain insights into customer behaviour. Background Most online shops follow a similar pattern in terms of the customer experience. Users are presented with a catalogue of products on a browser page or mobile application, and may make selections from these. Items […]
Introduction Apache Avro is primarily a data transfer mechanism, developed as part of the Apache Hadoop project and is similar to Thrift, Protocol Buffers, etc. Data is serialized into a compact binary format suitable for transmission across a network and/or storage to a persistence layer (i.e. Disk, DB, etc.). The data within the Avro format […]
A lot has happened since Jet’s debut last year. For instance Emin Demirci and many others have assembled some fun and interesting demonstration applications for Hazelcast Jet. Today we will walk through a simple Jet demo that shows how to implement a custom streaming source and sink and how to make streaming aggregations in Hazelast […]
*Rolando Santamaria Maso recently published a blog post entitled, In-Memory, Distributed Data Structures for the masses. * This article is about In-Memory Data-Structures. But this time, about the “magic” ones: Lists, Maps, Queues, Locks, Streams… that works distributed across multiple nodes and across different runtimes (Java, C#, C++, Node.js, Python, Go). Motivations I had the […]
The latest version of the Hazelcast Management Center (3.10) has just been released and we wanted to share with you some of the improvements that have been made in terms of functionality. The aim was to make it easier and much quicker to answer some common questions about a running cluster. There were two things […]
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