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.
AIOps is the use of machine learning and artificial intelligence to automate IT operations and reduce bottlenecks associated with technology needs.
Hazelcast’s low latency in-memory computing platform is ideal for AIOps applications.
Fast Stream Processing for data filtering and aggregation – many IT Ops tools charge for the data stored. Hazelcast Jet enables you to sample/filter/aggregate data ingested into AIOps and ITOps tools, helping reduce costs while still delivering real-time visibility.
Ultra-Fast Distributed Machine Learning – Bring the machine learning closer to applications across Multi-Cloud and Edge Architectures to drive further autonomous sense and response capabilities.
Edge-to-Cloud Ingest – Hazelcast Platform can run at the Edge, where the volumes of data are orders of magnitude greater, and therefore it isn’t practical to bring all the data back and aggregation, filtering, sampling and compression become a necessity.
Ingest and Correlation Z/OS to Multi-Cloud and Microservices – Today’s complex applications are interconnected, requiring cross-platform visibility to correlate problems across omni-channel applications in microservices and Core applications running on Z Platform. End-to-end visibility plus AIOps ensures accurate, fast root cause analysis and a complete picture of business impacts.
Hazelcast can be run at the edge, in any cloud (private, hybrid or multi-) to run and accelerate AIOps applications that need to process and analyze critical operations to create efficiency on technology deployments.
This video by Hazelcast senior solutions architect Sharath Sahadevan walks through a setup of WAN Replication on Google Cloud Platform.
Machine learning (ML) brings exciting new opportunities, but applying the technology in production workloads has been cumbersome, time consuming, and error prone. In parallel, data generation patterns have evolved, generating streams of discrete events that require high-speed processing at extremely low response latencies. Enabling these capabilities requires a scalable application of high-performance stream processing, distributed application of ML technology, and dynamically scalable hardware resources.
See how the distributed compute features of Hazelcast can be used to build a rule engine for low-latency, high-throughput transaction processing.
