This short video explains why companies use Hazelcast for business-critical applications based on ultra-fast in-memory and/or stream processing technologies.
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.
Now, deploying Hazelcast-powered applications in a cloud-native way becomes even easier with the introduction of Hazelcast Cloud Enterprise, a fully-managed service built on the Enterprise edition of Hazelcast IMDG. Can't attend the live times? You should still register! We'll be sending out the recording after the webinar to all registrants.
Looking for DEVELOPER specific use cases?
Hazelcast.org | IMDG Open Source
Today's engineering environment is driven by a massive increase in data input volumes and sources. In-memory technology provides the optimal solution to fuel innovation and modernize engineering processes.
In-memory brings unprecedented speed to today's complex, distributed development and manufacturing environments.
Run massively complex what-if testing scenarios by distributing the workload across secure clustered environments.
Eliminate single points of failure through distributed processing, reducing system down-time to essentially zero.
Cloud, multi-cloud, hybrid cloud, private cloud, multiple combinations thereof. In-memory technology can address your operational requirements.
Enable engineering excellence through the industry's leading in-memory technology.
With the integration of new data sources, such as IoT, today’s engineering environment is both complex and exciting. Attaching sensors to elements in the design and manufacturing phases of engineering new products opens up a wealth of new capabilities that can improve both the process and the end product. This new level of data ingest requires immediate processing in order to maximize effectiveness. And that is precisely what in-memory solutions from Hazelcast can deliver.
Some of the largest engineering firms in the world have discovered the benefits of the Hazelcast In-Memory Computing Platform: blinding speed from Hazelcast IMDG, and integrated streaming from Hazelcast Jet. Offered together as a platform, Hazelcast provides engineers with the fastest and most scalable platform solution available.
Events gathered per second
Real-time operational view
Through distributed architecture and Hot Restart
Process Management Depth
More easily manage the design and creation of new products through instant assimilation of data from production-line IoT devices.
Stability and Continuity
Hazelcast’s cloud-ready cluster architecture allows you to distribute loads and process complex requirements in parallel, minimizing resource stress and reducing downtime to zero.
Scale and improve your performance elastically, reducing hardware requirements and driving efficiency.
The leading in-memory solution for engineering.
Hazelcast IMDG is the leading Open Source in-memory data grid (IMDG). IMDGs are designed to provide high-availability and scalability by distributing data across multiple machines. Hazelcast IMDG enriches your application by providing the capability to quickly process, store and access the data with the speed of RAM.
Hazelcast Jet is an application embeddable, distributed stream processing platform for building IoT and microservices-based applications. The Hazelcast Jet architecture is high performance and low-latency-driven, based on a parallel, distributed core engine enabling data-intensive applications to operate at real-time speeds.
The benefits of moving to the cloud are well known and applicable to virtually every industry. Hazelcast offers our customers the flexibility to deploy to the cloud on their terms, whether it's a dedicated cloud, on-premise cloud, hybrid cloud, or private cloud.
High-Density Memory Store adds the ability for Hazelcast Enterprise HD IMDG to store very large amounts of cached data in Hazelcast members (servers) and in the Hazelcast Client (near cache), limited only by available RAM for extreme scale-up.
Stream processing is how Hazelcast processes data on-the-fly, prior to storage, rather than batch processing, where the data set has to be stored in a database before processing. This approach is vital when the value of the information contained in the data decreases rapidly with age. The faster information is extracted from data the better.
The speed of the Hazelcast In-Memory Computing Platform enables new levels of real-time predictive model servicing in support of delivering artificial intelligence solutions, as well as enabling real-time engineering and model retraining.
Are you a developer, software engineer or architect looking to apply in-memory technologies to your current architecture? Are you looking to deliver ultra-fast response times, better performance, scalability and availability? Are you seeking new tools and techniques to manage and scale data and processing through an in-memory-first and caching-first architecture?
Mainframe computers are used at many companies today, but the need for more cost-effectiveness is forcing changes. A popular strategy, mainframe optimization, enables lower mainframe costs due to the reduction in unnecessary MIPS. At the same time, it adds powerful new architectures related to cloud, microservices, and data streaming. An integration with IBM and Hazelcast […]
In this webinar, we’ll show how the microservices model can fall short in addressing the need for distributed access to shared data, and how Hazelcast can be used in a microservice architecture to solve some of the most difficult challenges facing developers and architects in building a robust distributed data architecture.
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.