Discover how to build a real-time fraud detection system using Flink SQL and Flinkflow. Learn to process high-velocity financial transactions, calculate rolling windows, and trigger instant alerts to prevent fraud before it happens.
AI models are only as good as the context you feed them. Learn how to use Flink SQL inside Flinkflow to build real-time feature stores, calculate rolling aggregations, and feed context-rich data into LLMs.
Flinkflow now supports Flink SQL as a first-class step type. Define schemas, write SQL queries, perform windowed aggregations, and join multiple streams — all from your YAML pipeline.
Raw security logs are useless without context. Learn how to perform sub-second temporal joins in Flinkflow to enrich firewall logs with asset and user metadata for instant threat detection.
Discover how to fuse live event streams with LLMs and AI models using Flinkflow’s Agentic Bridge. Learn how stateful memory and Async I/O power real-time AI decision engines.
Introducing native Apache Flink ML support in Flinkflow. Embed Estimators and Transformers directly in your YAML pipelines using dynamic reflection and StreamTableEnvironment.
Talweg