Real-time sensor data monitoring is crucial for industries such as manufacturing and IoT-enabled transport, where identifying anomalies in sensor readings can indicate potential issues like equipment failure or environmental hazards. This blog discusses anomaly detection in sensor data streaming through a Kafka topic, focusing on a scenario involving 100 sensors each sending a heartbeat every 5 minutes. Anomalies are defined as instances where data is missing for over 15 minutes, and the blog details a simulation of this setup over 100 days, using Python to introduce random sensor failures. The detection process employs DeltaStream and SQL functions like ds_lag_bigint to track sensor history, calculate time gaps between readings, and identify sensors exceeding the acceptable downtime, with results being suitable for integration into real-time decision-making dashboards in a production environment.