Here, we provide training materials for a course covering an example solution of IoT on Azure, Microsoft's cloud and cloud offerings.
This site is intended to be the main resource to an instructor-led course, but anyone is welcome to learn here. The intent is to make this site self-guided and it is getting there.
We recommend cloning this repository onto your local computer with a git-based program (like GitHub desktop for Windows) or you may download the site contents as a zip file by going to "Clone or Download" at the upper right of this repository.
For Instructor-Led:
- We recommend dowloading the site contents or cloning it if you can do so to your local computer.
- Complete the setup instructions
- Follow along with the classroom instructions and training sessions.
- When there is a lab indicated, you may find the lab instructions in the Labs (COMING SOON) folder.
For Self-Study:
- We recommend dowloading the site contents or cloning it if you can do so to your local computer.
- Go to Decks folder and follow along with the slides.
- When there is a lab indicated, you may find the lab instructions in the Labs (COMING SOON) folder.
Data Source and Ingestion
- Azure Web Jobs - scrapes data from a streaming data source and shuttles it to the Event Hub
- Azure Event Hub - receives the raw data from the Web Job
Data Preparation and Analysis
- Azure Stream Analytics - provides near real-time analytics and publishes results to Power BI dashboard, as well as, shuttles raw data to Azure Storage for archiving
- Azure Storage - stores the archived, raw streaming data for future processing
- Azure Data Factory - orchestrates data flow, running Hive scripts, calling out to the Azure Machine Learning service, and management of Azure SQL Database service
- Azure Machine Learning - returns predictions (here, future power consumption forecasts) based on inputs received
Data Publishing and Consumption
- Azure SQL Database - stores the results of the Azure Machine Learning service
- Power BI - dashboarding service containing aggregations provided by Azure Stream Analytics (data in motion) and Azure Machine Learning service results stored in Azure SQL Database (data at rest)
- Labs - hands-on exercises
- Decks - classroom slides
- Code - scripts
- Wiki - peruse and/or contribute
All code is licensed under the MIT license and we triage actively on GitHub. We enthusiastically welcome contributions and feedback. You can fork the repo and start contributing now.
Please check the releases for updates.
The CIQS team for the publishing of the Energy Demand Forecasting Solution.
Copyright (c) Microsoft Corp. All rights reserved. Licensed under the MIT License;
See license at this link.
We Value and Adhere to the Microsoft Open Source Code of Conduct.
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.