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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.
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Streaming analytics complements traditional analytics by adding real-time insight to your decision-making toolbox. In some circumstances, streaming analytics enables better business decisions by focusing on live, streaming data.
Azure Time Series Insights is a fully managed analytics, storage, and visualization service that makes it simple to explore and analyze billions of IoT events simultaneously. It gives you a global view of your data, which lets you quickly validate your IoT solution and avoid costly downtime to mission-critical devices.
Time Series Insights gives you the capacity to manage billions of data events from diverse and unpredictable IoT devices, without compromising performance.
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.
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