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.
With our recent release of Hazelcast IMDG 4.0, we would like to invite you to watch this video, where we will discuss the new features in this release at a high level and talk about how you can take advantage of them.
Hazelcast Cloud Enterprise is the new cloud-native managed service that allows you to quickly set up Hazelcast IMDG in a public cloud, fully managed for you by Hazelcast. This tutorial will walk through deployment of Hazelcast Cloud Enterprise on Amazon AWS.
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Looking for info on our live events? We're busy coordinating developer events at the moment, so please check back in a few days for the latest info. In the meantime, check out our free, on-demand training.
The business use case we'll use for this demonstration is a Trade Monitoring application for middle-office and back-office teams in a capital markets trading firm.
Back office analysts at capital market trading firms can now get on-demand, near-real-time summaries of the day’s trades.
The “cost versus risk” balance in capital markets trading firms can now be more efficiently addressed with modern technologies.
Analysts in the back office of capital markets trading firms need greater visibility on trades throughout the day. This reference architecture paper describes the use of the Hazelcast In-Memory Computing Platform to cost-effectively enable a near-real-time stock trading analysis solution.
Analysts in the back office of capital markets trading firms need greater visibility on trades throughout the day. This technical white paper describes a cost-effective solution that enables near-real-time querying on stock trading data.
Retail has become truly digital. Customers demand frictionless always-on experiences across all devices and all channels. Trends such as Touchless Retail and Buy-Online Pick-up Curbside or in-store are only accelerating in the current environment. At the same time, technologies such as Edge Computing, Machine Learning, and Augmented Reality are driving innovation and disruption. IBM, Intel, and Hazelcast are working together to deliver integrated solutions to meet these challenges.
IBM customers working on application modernization projects from on-premises installations to cloud architectures will need the infrastructure to take advantage of cloud-native capabilities. However, customers using IBM eXtreme Scale on-premises will need an alternative in-memory technology, as eXtreme Scale does not support cloud environments using Kubernetes and containers. Read this paper to see how Hazelcast delivers in-memory speeds to applications built on IBM Cloud Paks.
For decades, a sharp divide was drawn between information technology (IT) and operational technology (OT). The former is concerned with enabling data-driven business decisions, and the latter is concerned with monitoring and controlling physical processes, from factory floors to ATM machines, to connected vehicles. Only recently has there emerged a data-centric approach to IoT applications […]
The talk explains the basic principles of time-series data processing and how they affected the design of our TS storage. In this (very) practical talk we are going to discuss technical challenges and design decisions made during the process.
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