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!
Business software must be efficient, adaptable, and easy to use. When you need to process complex events that integrate to back-end infrastructure, support geographically distributed teams, microservices architecture can deliver the optimal solution.
Looking for DEVELOPER specific use cases?
Jet.Hazelcast.org | Jet Open Source
Leveraging Hazelcast for your microservices platform enables you to focus on solving business issues, rather than the infrastructure and development of your network communication. Hazelcast’s active development and user support make it a go-to platform for network distribution and in-memory data storage.
10% cost reduction
Operational savings from high-speed streaming data
Saving customers millions of dollars per week
Events aggregated per second
From extreme edge use cases
Migrating a complex system into a series of smaller, isolated and more manageable pieces allows the individual services to be deployed or replaced in isolation at a rapid pace.
This works particularly well in complex environments with technologies that were not originally designed to work together.
A platform of small interoperating services is always more resilient in the face of unexpected events, such as network outages.
The ability to replace specific components, rather than the entire application speeds up support, reduces downtime and ultimately results in happier end users.
Appropriate technology stacks can be used for each microservice to enable the best solution.
Microservices architectures are all about finding best-of-breed technology to produce an efficient and adaptable composite solution.
Microservices-deployed client server with a shared Hazelcast cluster provides a simple and easy transitional path to deploying microservices infrastructure.
If you need to keep your microservices contained to a specific process or application, Hazelcast can isolate it by embedding in specific, isolated clusters.
Hazelcast Microservices architecture can also be deployed in a client-server model with an isolated Hazelcast cluster per service.
Fixed IP addresses, Multicast, Apache jclouds, AWS, Azure, Consul, etcd, Eureka, Kubernetes, Zookeeper. Additionally, Hazelcast has clients for several programming languages, such as Java, C#/.Net, C/C++, Python, Node.js and Scala.
Hazelcast Jet is the leading in-memory computing solution for managing streaming data across your organization. It is an application-embeddable, distributed computing solution for building high-speed streaming applications, such as IoT and real-time analytics. Hazelcast Jet is
built on the foundation of Hazelcast IMDG, the leading in-memory data grid and one of the top data stores for microservices deployments.
This white paper walks through the business level variables that are driving how organizations can adapt and thrive in a world dominated by streaming data, covering not only the IT implications but operational use cases as well.
Machine learning (ML) is being used almost everywhere, but the ubiquity has not been equated with simplicity. If you solely consider the operationalization aspect of ML, you know that deploying your models into production, especially in real-time environments, can be inefficient and time-consuming. Common approaches may not perform and scale to the levels needed. These challenges are especially true for businesses that have not properly planned out their data science initiatives.
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.