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!
In a world where billions of devices are constantly streaming data into your IT systems, Hazelcast In-Memory Computing Platform is the ideal solution for managing your IoT ecosystem.
Looking for DEVELOPER specific use cases?
Hazelcast.org | IMDG Open Source
With billions of mobile users and potentially trillions of “things” generating data non-stop, there has never been a better time to deploy in-memory technology to support a wide range of high-speed streaming data sources, enabled by lightning-fast caching.
10% cost reduction
Streaming high-frequency data at sub-millisecond speed
From devices operating in complex, hostile, and remote environments
Events aggregated per second
With zero delays in processing, under extreme conditions
Hazelcast offers a light-footprint solution that enables lighting-fast edge processing applications across a broad range of industries.
The speed you need
From autonomous vehicles to remote drilling operations, streaming data at the edge requires incredible speeds, which can now be delivered by in-memory solutions.
Opportunities for innovation
When your processing and intake speeds accelerate by a factor of 1000x, new opportunities for service differentiation are suddenly possible. See how in-memory can move you ahead of your competitors.
Trying to track billions of things at once? Speed definitely matters. Stream data in at high volumes from multiple sources with Hazelcast Jet, and process the data in-memory with Hazelcast IMDG, all with millisecond speed.
An internet that has evolved to enable billions of devices and events, often occurring at the same time, requires scalability on a new level. Hazelcast provides distributed capabilities for seamless scaling.
Massive bursts of traffic? Hazelcast has you covered. Our ability to quickly scale up and down in response to external events optimizes your use of resources while maintaining high levels of customer satisfaction.
Providing millisecond response times, billions of times per second, with no noticeable impact on performance is what Hazelcast delivers. Our In-Memory solutions provide an end-to-end solution for the most demanding customers.
Edge computing complements your cloud deployments by addressing issues related to having data created in remote locations. While businesses today are still in the early stages of edge computing, the expectation is that there will be significant adoption in the next two years. Hazelcast believes now is a good time to explore edge opportunities, and supports such initiatives with in-memory technologies that help drive powerful edge deployments.
Future Grid works with several Australian utility companies to automate the processing of sensor and smart meter data which crosses energy networks. Their customers are collecting approximately 3 billion data points per day. In terms of daily post processing, this equates to 20 billion records as each record has multiple, individual data points --a massive scaling challenge. To make the most of this information, utility organizations need a real-time data aggregation and processing solution which enables them to make complex real-time decisions.
When Future Grid first tried to solve this problem, it used traditional relational databases. However, it soon became apparent traditional databases couldn’t cope with huge volumes of data in real-time, main issue being that they can’t execute algorithms against incoming data fast enough. Future Grid then decided to build its own solution combining Hazelcast IMDG® with Apache Cassandra’s persistence data store capabilities.
This white paper walks through the business level variables that are driving how organizations can adapt and thrive in a world dominated by streaming data, covering not only the IT implications but operational use cases as well.
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.