<ul><li>Internet of Things (IoT) data from sensors can provide real-time insights, predictive maintenance, and operational efficiencies.</li><li>Real-time analytics pipeline using Amazon Kinesis, Apache Flink, and Amazon EMR processes IoT sensor data.</li><li>Components include IoT sensors, Kinesis Data Streams, EMR with Flink, ML inference, and data storage.</li><li>Steps involve setting up Kinesis Data Streams, simulating sensor data, and configuring EMR with Flink.</li><li>Apache Flink application consumes Kinesis data, performs windowed aggregations, and integrates ML inference.</li><li>ML inference involves deploying models on SageMaker and invoking predictions from the Flink application.</li><li>Results can be stored in Amazon S3 and visualized using Amazon QuickSight for insights and monitoring.</li><li>Real-life use case includes predictive maintenance in manufacturing for anomaly detection and failure prediction.</li><li>Overall, the article guides building a scalable real-time analytics solution for IoT data processing and ML integration.</li></ul>

How To Process IoT Sensor Data with Windowed Aggregations and ML Inference

Discover more