Processing 10M samples a second to drive smart maintenance in complex IIoT systems
Geir Engdahl and Daniel Bergqvist explain how Cognite is developing IIoT smart maintenance systems that can process 10M samples a second from thousands of sensors. You'll explore an architecture designed for high performance, robust streaming sensor data ingest, and cost-effective storage of large volumes of time series data as well as best practices learned along the way.
|Talk Title||Processing 10M samples a second to drive smart maintenance in complex IIoT systems|
|Speakers||Geir Engdahl (Cognite), Daniel Bergqvist (Google)|
|Conference||Strata Data Conference|
|Conf Tag||Making Data Work|
|Location||London, United Kingdom|
|Date||April 30-May 2, 2019|
Today’s industrial IoT (IIoT) systems generate huge volumes of data—data that can be both difficult to both manage and make effective use of. Geir Engdahl and Daniel Bergqvist discuss a Cognite-developed IIoT setup, based on the Google Cloud Bigtable NoSQL database, that’s currently being used to process multivalue time series data at rates of up to 10M samples a second. This system is being used to drive ML-based production optimization and predictive maintenance in industrial systems comprising many thousands of sensors, replacing costly scheduled maintenance with targeted, proactive alerts to operators when system anomalies are detected. Geir and Daniel describe the key elements of the Cognite-developed system and demonstrate how to configure the underlying Google Cloud Bigtable NoSQL database to process high-velocity IoT data streams. They conclude by sharing some thoughts on data optimization to further increase the efficiency of the system.