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!
A cache is an important component of any application. In-memory caching can eliminate the bottlenecks for data access and processing to provide predictable latency and fast response time as an application’s user base grows. By implementing a cache-as-a-service across the organization, you can enable multiple applications to access a managed in-memory cache rather than slow disk-based databases.
By using Hazelcast as a cache-as-a-service, developers can separate the caching layer from the application layer. With cache-as-a-service, you can add caching to any application without worrying about the cache implementation.
Hazelcast provides a cache-as-a-service for scalable, reliable, and fast caching. Applications can use Hazelcast as a side-cache to their database or place the database in-line behind the caching service.
Fast, in-memory performance, ease of development, and scalability
Hazelcast provides a number of features that make it ideal as a cache-as-a-service. Hazelcast always stores and processes data in-memory for blazing-fast performance. It enables developers to leverage the cache with only minor modifications for many of their applications written in common languages. And Hazelcast elastically scales to handle more data and more user load thanks to its cloud-native architecture.
Enables the elasticity to grow or shrink as needed.
Lets you protect sensitive data with access controls, thus simplifying the multi-tenant requirements of cache-as-a-service.
Avoids garbage collection and provides predictable low latency across different applications without affecting each other.
Java, .NET, and C++ applications can use native client libraries to access the cache. For other languages, applications can use Hazelcast REST and Memcached interfaces.
Enables you to monitor cache server nodes, connected clients, and used caches. It provides notifications when cluster management operations take too long, making it easy to identify performance and stability issues.
Enables you to replicate the cache across multiple data centers to distribute the load to geographically remote users or to support disaster recovery strategies.
Hazelcast provides two standard APIs in its JCache and Map interfaces. JCache is standard for JSR 107 and the Map interface is based on java.util.Map. Developers can use MapStore and CacheStore, respectively for Map and JCache, to make Hazelcast automatically load and store data from a database.
Allows each app server to cache hundreds of GB locally, to improve data access to microseconds instead of milliseconds.
DRAM is dirt cheap. That’s why in-memory databases, analytics, and data grids are surging in popularity among firms that have an insatiable need for performance and scalability. But, databases, analytics platforms, and data grids target very different use cases. In-memory data grids, in particular, are often misunderstood because they support an extensive set of use cases that often overlap other technologies. Join guest speaker Mike Gualtieri, Principal Analyst at Forrester Research, Greg Luck, CEO of Hazelcast®, and Ken Kolda, Software Architect of Ellie Mae on this radio-show style webinar to boost your in-memory IQ.
This pragmatic eBook will teach you about the important features of Hazelcast IMDG® while bringing you up to speed on the latest features in Hazelcast IMDG 3.9.
New Chapters! This 3.9 edition of Mastering Hazelcast IMDG features a new chapter on Hazelcast Jet®, and a new chapter on the Hazelcast IMDG Consistency and Replicaton Model
Everything you need to know to successfully implement maps in Hazelcast IMDG.
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.