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.
Finance, risk, operations, compliance, and treasury teams are still anchored to time-delayed batch data processing on end-of-day positions. The batch data processing paradigm of the middle- and back-office functions means that they are operating with lapses of time in which the activity is unknown. The ability to spot and immediately react to an issue that could have a systemic impact on the organization is severely limited. The goal would be to get those gaps of the unknown as small as possible.
This paper discusses a better alternative to today’s batched back-office analysis systems. You can instead run on-demand analytics only on the relevant data for your specific query. This gives you near-real-time analytics in a cost-effective manner.