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APACHE STORM
Distributed real-time stream processing at scale
Apache-2.0
ABOUT
Processing real-time data streams for AI inference and monitoring requires systems that can handle high throughput with low latency while providing fault tolerance. Apache Storm processes unbounded data streams in real-time using distributed topologies of spouts and bolts, making it possible to run streaming feature engineering, real-time model scoring, and online learning pipelines at production scale.
INTEGRATION GUIDE
1. Run real-time feature engineering on streaming data for online ML model inference
2. Build streaming ETL pipelines that prepare and transform data for model training
3. Process live telemetry streams for AI model monitoring and drift detection
4. Implement online learning pipelines that update models in real-time from data streams
5. Orchestrate real-time alerting and anomaly detection across distributed data sources
TAGS
stream-processingreal-timedata-pipelinedistributed-systemsstreaming