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.
Microservices architectures make a lot of sense in today’s complex, data-intensive deployments. If you design large applications as a cohesive group of smaller tasks, you can create a modular, easy to maintain, fault-tolerant, and scalable system that you can continue to expand and enhance for years to come.
The use of in-memory and streaming technologies is becoming a necessity for today’s advanced microservices. Hazelcast can especially help in your microservices projects with high performance and efficient inter-service communications.
High performance. Hazelcast IMDG provides an in-memory store that can easily be embedded into your microservices deployment, giving you fast data lookups as well as a medium for saving state. The in-memory advantage ensures you are not adding unnecessary latency to your pipeline when reading and writing data.
Efficient communications. The next generation of microservices are using streaming technologies to simplify inter-service communications. You can also use IMDG or even Apache Kafka as a messaging system to let a microservice pass its data to the next, instead of using traditional REST APIs or databases that require writing coordination code. Develop microservices using Hazelcast Jet, a stream processing engine, which offers an API that can read messages from your messaging system of choice, process them, and then pass them back to the messaging system for the next stage of the microservices pipeline.
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.
Are you ready to take your algorithms to the next steps and get them working on real-world data in real-time? We will walk through an architecture for taking a machine learning model into deployment for inference within an open source platform designed for extremely high throughput and low latency.
The use of streaming technologies in microservices is an emerging trend that you should consider. And combining streaming with in-memory technologies lets you deploy and run your systems faster.
The first generation of microservices was envisioned as stateless request-response endpoints. But it's now clear that microservices must often maintain some state. Join us for this webinar where we will discuss why today's business solutions need a next-generation microservices 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.