Extract, Aggregate, and Visualize Real-Time IoT Data with Power BI
Question
Using real-time data ingested from hundreds of sensors connected to your IoT hub, you need to extract, aggregate and send some key data to PowerBI dashboards for real-time visualization where, besides the real-time values, you also need to show trends from the past 14 days.
You need to use Stream Analytics to feed data to Power BI.
You notice that trends only cover the past 7 days.
Which of the following is the recommended, cost-effective solution for this use case?
Answers
Explanations
Click on the arrows to vote for the correct answer
A. B. C. D.Correct Answer: C.
Option A is incorrect because both jobs will use the streams of the same IoT Hub with the same retention time, which is probably set to 7 days (which is the upper limit), i.e.
data beyond 7 days still not will be available.
Option B is incorrect because the maximum retention time for IoT hubs is 7 days, with the default of 1 day.
For standard hubs, higher values cannot be set.
Option C is CORRECT because Stream Analytics is great for feeding real-time data to Power BI, while data can be sent to Azure Storage for historical analysis, e.g.
for trend analysis.
Option D is incorrect because dedicated IoT hubs provide a retention time of 90 days.
Although the option is valid in itself, it is not the recommended solution to use for historical data.
References:
The recommended, cost-effective solution for the given use case would be Option C - Use Stream Analytics for real-time dashboards and feed trend charts from a Storage container.
The reason for this is as follows:
Option A - Use separate Stream Analytics jobs for real-time dashboard and for the trend display: While this approach would work, it would require setting up and managing two separate Stream Analytics jobs, which can increase the overall cost and complexity of the solution.
Option B - Set the IoT Hub retention time to 14 days: While increasing the retention time of the IoT Hub would ensure that the data is available for 14 days, it does not solve the problem of displaying trends for the past 14 days in Power BI. Additionally, increasing the retention time can increase the cost of the IoT Hub.
Option D - Use a dedicated IoT Hub: Using a dedicated IoT Hub would not solve the problem of displaying trends for the past 14 days in Power BI. Additionally, setting up and managing a dedicated IoT Hub can increase the overall cost and complexity of the solution.
Option C - Use Stream Analytics for real-time dashboards and feed trend charts from a Storage container: This option would involve using Stream Analytics to extract and aggregate real-time data from the IoT Hub, and then sending it to Power BI for real-time visualization. For displaying trends from the past 14 days, the data can be stored in a Storage container, which can then be accessed by Power BI. This approach is cost-effective as it requires only one Stream Analytics job and a Storage container, and does not require increasing the retention time of the IoT Hub or setting up a dedicated IoT Hub.
In conclusion, the recommended, cost-effective solution for the given use case is Option C - Use Stream Analytics for real-time dashboards and feed trend charts from a Storage container.
