Companies need a data-processing solution that increases the speed of business agility, not one that is complicated by too many technology requirements. This requires a system that delivers continuous/real-time data-processing capabilities for the new business reality.
Stream processing is a hot topic right now, especially for any organization looking to provide insights faster. But what does it mean for users of Java applications, microservices, and in-memory computing?
In this webinar, we will cover the evolution of stream processing and in-memory related to big data technologies and why it is the logical next step for in-memory processing projects.
Setting up servers and configuring software can get in the way of the problems you are trying to solve. With Hazelcast Cloud we take all of those pain points away.
Watch this webinar to learn how you can instantly fire up and then work with Hazelcast Cloud from anywhere in the world. With our auto-generated client stubs for Java, Go, Node.js, Python and .NET, we can have you connected and coding in less than a minute!
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industry’s leading in-memory computing platform.
The in-memory speed you count on, with the convenience and scalability of cloud.
The industry's fastest, most scalable in-memory data grid, where speed, scalability and continuous processing are the core requirements for deployment.
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Hazelcast.org | IMDG Open Source
An IMDG (in-memory data grid) is a data structure or layer that resides in random access memory (RAM), as opposed to a hard drive or a database. Due to recent innovations, it’s now possible to store terabytes of data in RAM, as well as to distribute this data across multiple nodes in a network or cloud while having it run as a single system.
The primary advantage is speed, which has become critical in an environment with billions of mobile, IoT devices and other sources continuously streaming data. With all relevant information in RAM, in an IMDG, there is no need to traverse a network to remote storage for transaction processing. The difference in speed is significant – minutes vs. sub-millisecond response times for complex transactions done millions of times per second.
Enables the largest data sets to run efficiently in an in-memory cluster across your most popular data APIs.
Provides a bird’s-eye view of all cluster activity, along with configurable watermarks for alerts through a web-based user interface and cluster-wide JMX and REST APIs.
Whether the restart is a planned shutdown or a sudden cluster-wide crash, Hot Restart Store allows full recovery to the previous state of configuration and cluster data.
Upgrades cluster nodes and versions without service interruption.
Provides standards-based Java Authentication and Authorization Service (JAAS) and interoperable encryption, authentication, and access control checks to mission-critical applications.
Synchronizes multiple Hazelcast clusters in different datacenters for disaster recovery or geographic locality, and can be managed centrally through Management Center.
Switch between alternate clusters on demand.
Automatic failover to a disaster recovery cluster.
Enables your applications to run in the most popular enterprise container management environments, Pivotal Cloud Foundry and OpenShift Container Platform.
24×7 service and support with a one-hour service-level agreement, as well as maintenance and hot patch fixes.
Competent fraud detection systems can help organizations gain a clearer view of entities, relationships, and hidden patterns as they deal with financial crimes, including payment card fraud, anti-money laundering and anti-terrorist financing. Hazelcast can store account details, recent transaction history and scoring all in one place for powerful, comprehensive checks, live within the request. Short SLAs measured in milliseconds means that these checks must be done very fast. If high-speed fraud detection is your concern, Hazelcast’s in-memory data grid is a perfect fit.
The UK’s largest Internet of Things (IoT) network serves more than 200,000 homes with a system that allows users to remotely control their heating and hot water temperature from their mobile device or on the internet. This completes 20,000 writes per second in a 20-node Hazelcast IMDG cluster with plenty of spare capacity and an average latency of under 1 millisecond.
Hazelcast In-Memory Data Grid is used as the back-end infrastructure for many IoT deployments. Everything from in-home thermostats and lighting systems to industrial sensors, such as smart meters and in-store e-Commerce systems.
See how easy it can be to add Hazelcast In-Memory Data Grid to your business. Start your free 30-day trial now.
A high-level overview of Hazelcast IMDG technology and operations
Start using Hazelcast IMDG right away with this quick demo.
How to configure Hazelcast IMDG
Includes the full feature list for Hazelcast IMDG® Enterprise HD, Hazelcast IMDG Enterprise, and comparison to Hazelcast IMDG Open Source.
In-Memory Databases (IMDB) and In-Memory Data Grids (IMDG) are two technologies that address real-time computing and big data needs without having to start over with an entirely new set of IT systems. While they may sound similar, the differences are significant and understanding your options will help you make better decisions.
Your business operates in an environment with constantly evolving technology. For business and IT leaders, it can be overwhelming to keep up with new developments and determine what is critical to implement immediately, and what technologies can or should wait. In-memory processing technologies are becoming pervasive and have a direct and indirect effect on how a business operates. Hazelcast has created this whitepaper to discusses these technologies and the respective implications.
This white paper, written by Java Champion Ben Evans, provides an introduction for architects and developers to Hazelcast®’s distributed computing technology.
Whether you're interested in learning the basics of in-memory systems, or you're looking for advanced, real-world production examples and best practices, we've got you covered.