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!
Hazelcast IMDG® can be used as cache for applications based on the Spring Framework for distributing shared data in real time.
The Spring Framework provides an abstraction layer for caching providers, which is a first-class citizen in Hazelcast IMDG. Within the default Hazelcast® download /lib directory, there is a JAR file titled hazelcast-spring.jar, which is included in the hazelcast-all.jar. By including hazelcast-all.jar or hazelcast-spring.jar, Hazelcast can be used as an implementation of the Spring cache abstraction layer and can annotate methods as @Cacheable.
As of version 3.1, the Spring Framework provides support for adding caching into an existing Spring application. To use Hazelcast as a Spring cache provider, you simply define a com.hazelcast.spring.cache.HazelcastCacheManager bean and register it as a Spring cache manager.
<cache:annotation-driven cache-manager="cacheManager" />
<bean id="cacheManager" class="com.hazelcast.spring.cache.HazelcastCacheManager">
Hazelcast has a built-in integration with Spring to easily plug in caching. And it is more than just a simple cache for your Spring environment, as it provides capabilities to run a high-volume production web application with business-critical requirements.
Hazelcast is designed to deliver the highest throughput and lowest latencies. Benchmark reports show we are faster than other popular in-memory technologies.
Hazelcast provides authentication, authorization, and encryption capabilities to ensure your data is protected from unauthorized access.
Hazelcast elastically scales by expanding or shrinking the cache to accommodate your needs. You can grow your Hazelcast cluster to extremely large sizes, so you can use it for multiple purposes at the same time including application caching, batch processing, and stream processing, in addition to the Spring cache.
Hazelcast has built-in replication for high availability and disaster recovery to ensure business continuity of the high speed caching layer. You can use the WAN replication feature to distribute cached data across remote locations to put the data near the end users.
The most comprehensive solution for data at rest and data in motion.
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.
Needing more performance from your Java applications? Is latency causing you stress?
If so, take a look at Spring's Cache Abstraction!
Join us in “Caching Made Bootiful: The Hazelcast® Way,” a code-driven webinar, as we demonstrate how to integrate Hazelcast Distributed Caches into your Spring (Boot) applications using Spring's Cache Abstraction and Auto Configuration features.
JCache, the new standard API for cache access, comes with a full set of annotations that Spring understands since version 4! Adding annotations to a method can achieve massive speed improvements in applications with high-latency. This webinar will give you an introduction on how to use these standard annotations in your Java and Spring applications, when to use which annotations, and what is automagically happening under the covers. You will leave inspired to add 12 characters to your code.
Spring Data is a popular tool in the Spring ecosystem that brings a unified approach to data access that enables
applications to treat data sources as interchangeable. An application using the Spring Data paradigm can make
CRUD and query operations through a standard repository interface and can largely ignore the specifics of the
The Spring Data for Hazelcast® module allows this behavior using Hazelcast as the backing store, implementing the @Repository interface and with querying capabilities.
From a developer's perspective, repository access is standardized, there's no need to know how that the backend
is Hazelcast, simplifying their personal tech stack.
From an architecture's viewpoint, all the benefits of Hazelcast (speed, scaling, resilience, etc) can be obtained for minimal effort.
In this guide, you will learn how to use Hazelcast distributed caching with Spring Boot and deploy to a local Kubernetes cluster.
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.