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.
Credit value adjustment risk calculation is a common example of a massive, parallelized computation system in the financial services industry. Computing costs are high but necessary, considering the importance of the calculations from both a financial risk and regulatory perspective. However, distributed, cloud-based systems can change that by reducing overall costs with a flexible, pay-per-use model that avoids up-front spending on resources that are not used 24/7.
This reference architecture whitepaper discusses why a cost-effective, “straight-through-processing” credit value adjustment risk calculation system is a worthy pursuit for financial services companies. A combination of in-memory storage, stream processing, and distributed computing serves as a model for a large-scale calculation that involves tens of thousands of CPU cores.
While the use case in this paper focuses on risk calculation, this architecture and the associated technologies apply to other massive deployments in a variety of industries.
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