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DataFreemiumOpen Source
HAZELCAST
Real-time stream processing and in-memory data grid
Apache-2.0
ABOUT
AI pipelines that need real-time processing — streaming feature computation, online inference aggregation, or event-driven model scoring — require a platform that combines fast data access with stream processing. Separate databases and stream processors add latency and complexity. Hazelcast solves this by unifying an in-memory data store with embedded stream processing in a single horizontally scalable platform. Data is stored partitioned across the cluster with near-line access, and computations run where the data lives — eliminating serialization overhead and network round trips for real-time ML workloads.
INTEGRATION GUIDE
1. Compute streaming features in real time for online ML inference without round-tripping through a separate database
2. Aggregate and score incoming events against ML models with sub-millisecond latency for fraud detection and personalization
3. Build real-time feature engineering pipelines that transform and serve features directly from streaming data
4. Deploy a distributed cache layer for model predictions, embeddings, and lookup tables across an inference cluster
5. Run continuous ML inference on event streams with exactly-once processing semantics and elastic scalability
TAGS
stream-processingin-memory-data-griddistributed-computingcachingdata-platformjava