Hazelcast

Hazelcast reduces wait times and blockages. Your data streams can be ingested, contextualized, and enriched as they flow from any source to any destination. This enables applications to present data in real-time, allowing applications to act immediately on information that is highly valuable at the time of creation, but that quickly degrades over time.

Hazelcast also helps add context to data. This is crucial because it adds relevance to data that may have unclear meaning at first. Context provides evidence-based understanding of that data - so you have the best chance of making the best decisions. You can merge operational data with reference data, correlate, and discover valuable insights

We reduce wait times by building technologies that let you remove unnecessary delays. Those technologies are driven by 3 principles:

1. Stream processing lets you act on data in individual units as it is created, so you don’t have to wait for it to be stored in a database before you do something with it.
2. In-memory computing lets you access data in the fastest way possible – while it is stored in RAM. This means you don’t have to wait for data to be retrieved from slower media.
3. The integration of these two highly optimized computing frameworks creates an environment that further reduces delays, by eliminating the inefficiencies of bolting together two disparate technologies, which delays via network calls. This creates context for your data to give you the insights you need to act now instead of later.

The Hazelcast Platform entails:

• A downloadable software package or a cloud-native, fully managed service, to support on-premises, public cloud, and private cloud (as well as hybrid- or multi-cloud)

○ Support across the many deployment environments helps with geo-distribution, higher levels of availability, and even data migration (especially from on-premises to the cloud)

• Real-time performance at scale regardless of workload size

○ Elastic scalability for zero-downtime scale up to meet times of peak demand, and scale down to avoid overpaying for resources during other time

○ Lightweight runtime to run on small edge devices

○ Performance/efficiency to handle billions of events per second in a relatively small hardware footprint

• Integrated storage engine with highly parallel execution engine; a combination not available in any other integrated product

○ In-memory speed that also provides additional capacity via overflow to disk when data sizes exceed memory capacity

Stream and batch processing engine that finds and exploits opportunities for concurrency, maximizing parallelism without the need for developers to master complex thread-aware programming models.