Note: If you have already deployed this solution, click here to view your deployment.
For more details about how this solution is built, visit the solution guide in GitHub.
Estimated provisioning time: 20 minutes
The Cortana Intelligence Suite provides advanced analytics tools through Microsoft Azure – data ingestion, data storage, data processing and advanced analytics components – all of the essential elements for building a tank level forecasting solution.
This solution combines several Azure services to provide powerful advantages. Event Hubs collects real-time tank level data. Stream Analytics aggregates the streaming data and makes it available for visualisation. Azure SQL Data Warehouse stores and transforms the tank level data. Machine Learning implements and executes the forecasting model. Power BI provides a visualisation of the real-time tank level as well as the forecast results. Finally, Data Factory orchestrates and schedules the entire data flow.
The ‘Deploy’ button will launch a workflow that will deploy an instance of the solution within a Resource Group in the Azure subscription you specify. The solution includes multiple Azure services (described below) along with a web job that simulates data so that immediately after deployment you have a working end-to-end solution.
After deployment, see the post-deployment instructions here.
Technical details and workflow
- The data feeds into the Azure Event Hubs and Azure SQL Data Warehouse service as data points or events, which will be used in the rest of the solution flow.
- Azure Stream Analytics analyses the data to provide near real-time analytics on the input stream from the event hub, and publishes directly to Power BI for visualisation.
- The Azure Machine Learning service is used to make forecasts on the tank level of a particular region given the inputs received.
- Azure SQL Data Warehouse is used to store the prediction results received from the Azure Machine Learning service. These results are then consumed in the Power BI dashboard.
- Azure Data Factory handles orchestration and scheduling of the hourly model retraining.
- Finally, Power BI is used for results visualisation, so that users can monitor the tank level from a facility in real time and use the forecast level to prevent spillage.
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