In Part I, I went over the overview of use cases, scale, ingestion and storage. In Part II, I will cover the real-time streaming of data to longer-term storage for analytics. I will also go over the configuration of Power BI for real-time Q&A analytics.

Data Movement

Once the data arrives in an Event Hub, it won’t stay for long. Event Hubs have a configurable retention period of 1-7 days, so you need to “read” the data out fairly quickly. To do this, you need a service that can scale to millions of records per second and offer the flexibility to interpret the data along the way by minor forms of aggregation. For this, Azure Stream Analytics is the perfect solution.

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If you’ve been following the cloud buzz, you’ve inevitably heard of IOT or Internet Of Things. At a high level, the concept is simple: connect a bunch of devices that weren’t previously accessible and use intelligent data to inform other devices or take some action. A couple simple examples might include sensors on doors, vending machines or robots. Where in the past, a repair person might have to repair a motor on a scheduled basis, sensor data could inform that individual to take action sooner (or postpone) based on what the data is saying.

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