This short video explains why companies use Hazelcast for business-critical applications based on ultra-fast in-memory and/or stream processing technologies.
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.
Now, deploying Hazelcast-powered applications in a cloud-native way becomes even easier with the introduction of Hazelcast Cloud Enterprise, a fully-managed service built on the Enterprise edition of Hazelcast IMDG. Can't attend the live times? You should still register! We'll be sending out the recording after the webinar to all registrants.
Integration is your differentiation!
In the modern world what makes the difference is the shelf-life of your data analysis. When you run analysis on your data to derive insights, these insights rely on the recency of the data. Deriving one from the other introduces latency; the insights go stale as time passes and their value diminishes. After all, who wants to know yesterday’s weather. We want to know today’s and better still tomorrow’s. What we need to do is derive our insights continuously. As one data record changes, so does the derivation. Then the insights stay up to date.
By way of a demo, I’ll show how this is done using a Trading Platform where we wish to see all trades and trade summary information together. All you need is a stream processing engine integrated with a fast data store. Changes in core data in the data store flow through streaming analytics to create derived data. If it’s all integrated, performance is excellent for high volume and low latency.
QCon Plus Speaking Session featuring Neil Stevenson