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!
The cache is an important component of any application. In-memory caching can eliminate the bottlenecks of applications and provide predictable latency and fast response time to reach the growing mass of users. By implementing a Cache-as-a-Service across the organization, you can enable multiple applications to access managed in-memory cache rather than slow disk-based databases.
By separating the caching layer from the application layer, developers can isolate the caching infrastructure from the application. Any applications or groups within the organization can add caching to their application without worrying about the cache implementation. Hazelcast®, the leading open source in-memory data grid, enables you to implement a Cache-as-a-Service layer by providing a scalable, reliable, fast caching solution. Applications can use Hazelcast as side-cache to their database or hide the database behind the caching service and allow Hazelcast to load data from an RBDMS, NoSQL or other storage.
The following Hazelcast features are very useful for implementing 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. Provides notifications when cluster management operations take too long, helping to identify performance and stability issues.
Hazelcast provides two standard APIs. With MapStore and CacheStore, respectively for Map and JCache, Hazelcast can automatically load and store data from a database.
Allows each app server to locally store hundreds of GB of cached data to improve data access to microseconds instead of milliseconds.
The leading in-memory solution for your digital ecosystem.
Hazelcast IMDG is the leading Open Source in-memory data grid (IMDG). IMDGs are designed to provide high-availability and scalability by distributing data across multiple machines. Hazelcast IMDG enriches your application by providing the capability to quickly process, store and access the 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 information contained in the data decreases rapidly with age. The faster information is extracted from the data better.
When microseconds can mean the difference between success and failure, caching solutions deliver blinding speed with scalable and flexible management of data. In this white paper, we cover specific of caching solutions, where and why an enterprise would be interested, its application across multiple industries and its impact on data management, including Artificial Intelligence and Machine Learning use cases.
This white paper, written by Java Champion Ben Evans, provides an introduction for architects and developers to Hazelcast®’s distributed computing technology.
With $18.3 Billion in annual online sales, this global provider of personal computers
and electronics has one of the most highly trafficked eCommerce web sites in the world second only to Amazon.com. Burst traffic during new product introductions (NPI) are at an extreme scale, as are sales on Black Friday, Cyber Monday and over holidays.
This unique combination of world-class brand experience and extreme burst performance scaling led this eCommerce giant to examine In-Memory Computing solutions as a way to achieve the highest possible price-performance.
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.