Difference between Stream Analytics and Time Series Insights

Question

In the context of Azure IoT hub, when would one use Stream Analytics over Time Series Insights?

The product pages and documentation for both indicates they are heavily geared for IoT/data applications. However, I'm not clear on the differences.

The use case I have is both real time monitoring as well as ETL analysis. Could (or even should?) the two be used together?

One immediate difference I can see is that Time Series Insights stores the data whereas Stream Analytics (I think) would need the developer to integrate storage.

Answer 1

In short, stream analytics is about transforming, filtering and aggregation of data and time series insight is about visualising (stored) data.

Data passed through stream analytics is typically forwarded to resources like power bi (for realtime monitoring) or storage like a database for later analysis or processing.

One immediate difference I can see is that Time Series Insights stores the data whereas Stream Analytics (I think) would need the developer to integrate storage.

This is a correct statement. TSI is a data store, but its purpose is to create an environment to (visually) analyze that data. ASA cannot be used to analyze data on its own.

You could use ASA to transform the data and have the data send to Event Hub. That same Event Hub can then be used as a data source for TSI.