New Capabilities

Predictive intelligence from machine learning has the potential to change everything in our day to day experiences, from education to entertainment, from travel to healthcare, from business to leisure and everything in between. Modern ML frameworks are batch by nature and cannot pivot on the fly to changing user data or situations. Many simple ML applications such as those that enhance user experience, can benefit from real time robust predictive models that adapt on the fly.

Most large organisations run multiple sites, on-premise or in the cloud, to provide the assurance of business continuity. As far as we're concerned, these organisations will also be running in-memory cache and data grids. And now these organisations are looking towards PAAS platforms for simplified infrastructure management and cost savings.

In this talk we'll look at doing all three at once, using multiple Hazelcast IMDGs hosted in multiple Pivotal Cloud Foundry (PCF) installations.

It is well-known that cache performance is non-linear in cache size and the benefit of caches varies widely by workload. Irrespective of whether the cache is in a storage system, database or application tier, no two real workload mixes have the same cache behavior! Existing techniques for profiling workloads don’t measure data reuse, nor do they predict changes in performance as cache allocations are varied.

Apache Ignite is a high-performance, integrated and distributed in-memory platform for computing and transacting on large-scale data sets in real-time, orders of magnitude faster than possible with traditional disk-based or flash technologies. Ignite as a collection of independent, well-integrated, in-memory components geared to improve performance and scalability of your application. Some of these components include: Advanced Clustering, Data Grid, SQL Grid, Streaming & CEP, Compute Grid, Service Grid, Ignite File System.