This is an application talk targeted at users of Apache Beam to illustrate how a combination of stateless, stateful, and windowed streaming transformations can be used to support arbitrarily complex real-time analysis of manufacturing time-series data.
At Oden, we are focused on the ingest and analysis of data from connected manufacturing equipment, context from manufacturing execution systems, and input from operators on the manufacturing factory floor. We run several real-time analytics using ML models deployed on Apache Beam to provide access to patterns, alerts, and process optimization insights to end-users. As part of this, we leverage Apache Beam’s features to perform stateless, windowed, and stateful calculations, transform and contextualize manufacturing tag values into meaningful metrics that speak about a process, and extract features that are input to the ML models.
In this talk, we present how we realize an “Algebra of Streaming Metric Calculations” on real-time data using a combination of stateless, stateful, and windowed operations, and how these calculations are leveraged to provide insights into the manufacturing process. This includes: