Showing how AWS Managed Service for Apache Flink can process real-time operational data streams leveraging stateful processing, windowing, and pattern matching to detect complex anomalies, performance deviations, or unusual events. When an anomaly is detected, relevant context gathered by Flink is packaged and sent to an AI service (like AWS Bedrock, or another AI/ML model) to generate a natural language explanation, interpretation, or summary of what happened and why it might be significant. This transforms cryptic alerts into actionable insights, making it easier for operators, business users, or developers to understand unexpected situations.
Level: 200

Target Audience: Data Engineers, ML Engineers, SREs, DevOps, Business Analysts interested in proactive monitoring. Technical Level: Intermediate/Advanced. Preferred Duration: 40-50 minutes. Requires familiarity with streaming analytics, basic anomaly detection concepts, and understanding how to interact with AI/ML APIs. Key Takeaway: Learn how to build systems that not only detect issues in real-time but also automatically explain them using AI, improving incident response and understanding.