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!
Overview
Today’s applications, whether they are newly developed or long-proven, need predictable latency and fast response times to reach their growing mass of users.
JCache standardizes caching for the Java platform. It is a common mechanism to create, access, update, and remove information from caches. It accelerates mainstream adoption of in-memory computing by giving all Java developers an easy way to access memory from within Java. Enterprises greatly benefit from the increased speed and scalability of applications that take advantage of JCache, and can change providers without rewriting their applications or maintaining a proprietary bespoke cache abstraction layer.
Hazelcast enables organizations to seamlessly integrate with JCache. The JCache caching layer API—specified by the Java Community Process (JCP) as Java Specification Request (JSR) 107—provides a standard set of operations specialized for caching use cases. Organizations can use these operations to scale out applications and manage high-speed access to frequently used data. Hazelcast smoothly achieves its caching potential with a 100 percent compliant implementation that transparently registers with the JCache subsystem.
Hazelcast provides multiple ways to use JCache, depending on your deployment strategies, security considerations, and usage patterns. You can use Hazelcast as a client-server or a cluster-only architecture.
Client-server architectures are used for high-security environments where:
With both client-server and cluster-only, Hazelcast offers:
for the highest throughput and lowest latency to accelerate your applications.
so the cache can be sized up and down.
with backend systems such as databases using JCache CacheStore and CacheLoader interfaces.
through your web browser with Hazelcast Management Center.
The most comprehensive solution for data at rest and data in motion.
Solutions
Hazelcast IMDG is the industry’s leading in-memory data grid (IMDG). IMDGs are designed to provide high-availability and scalability by distributing data across multiple machines. Hazelcast IMDG enriches applications by providing capabilities to quickly process, store and access 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.
Resources
You created a Java EE application using a REST front-end on top of relational database using JPA. Now you need to make it highly available and scalable across a large number of machines. This webinar will start with a simple JAX-RS/JPA application. We will turn this standard Java EE application, step by step, into a fully clustered application using a CDI extension and producers to integrate Hazelcast®, as a JCache provider. To do that we will study the data model and how to persist it efficiently.
Delivered in partnership with GameDuell, this technical talk by Greg Luck provides an in depth introduction to caching, with a particular focus on JCache and Hazelcast®.
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.
