StreamKernel.io packages AI, policy, and delivery in one JVM runtime
StreamKernel-io is a newly released source-available JVM pipeline kernel designed for policy-aware, benchmarkable operational data movement, offering in-process AI enrichment, policy enforcement, transformation, and multi-destination delivery. This GitHub repository provides the runtime boundary, SDK contracts, and benchmark evidence, explicitly withholding private AI model artifacts and commercial implementation details for proprietary licensing.
Key Takeaways
- The repo is public on GitHub under IntuitiveDesigns and carries the StreamKernel.io v0.2.0 initial source-available release.
- StreamKernel combines source plugins, a core orchestrator, policy plugins, transformer chains, sink plugins, DLQ routing, and metrics inside one JVM process.
- Its AI path includes DJL, ONNX, and MLflow integration for in-process enrichment and model promotion/rollback evidence.
- Public benchmark material includes Pulsar-to-Kafka burst-drain runs, Delta Spark local benchmark files, and benchmark matrices in benchmark-runs/.
- The repository explicitly withholds private AI model artifacts and commercial implementation details for proprietary licensing.
Why It Matters
StreamKernel.io is aimed at the operational gap between transport, policy, inference, and delivery: one runtime boundary instead of a chain of sidecars, model servers, and sink-specific glue. The public repo gives enough to inspect the architecture, benchmark evidence, and plugin contracts, while keeping model artifacts and implementation code behind a commercial line. That matters for teams trying to evaluate whether AI enrichment belongs inside the event path, not beside it. The concrete signal to watch is the public benchmark suite: Pulsar burst-drain, Delta Spark, and AI enrichment rows in benchmark-runs/.
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