Air Quality Real-Time Analytics is a hands-on Microsoft Fabric project course designed to help you build a practical end-to-end streaming analytics solution using real-world environmental data. In this course, you will work with near real-time air quality data from the OpenAQ API and learn how to design a complete analytics pipeline using Eventstream, Eventhouse, KQL, semantic modeling, and reporting.
You will start by understanding the data source and identifying how location metadata and latest sensor measurements can be ingested into Microsoft Fabric. From there, you will build a medallion-style architecture inside Eventhouse and KQL Database, organizing the solution into Bronze, Silver, and Fact/Calc layers. You will also create supporting dimension tables such as location, pollutant, and date, then use KQL scripts to perform transformations, feature engineering, and calculations like pollution categorization, daily change, and moving averages.
This course is ideal for learners who want more than theory. It focuses on implementation, helping you understand how real-time and near real-time analytics workloads are built in Fabric using a meaningful use case. By the end of the course, you will have a portfolio-ready project that demonstrates API ingestion, Eventstream integration, KQL-based modeling, automated pipeline scheduling, and reporting-ready data structures. This is a great project for Microsoft Fabric learners, data engineers, analytics engineers, and Power BI professionals who want hands-on experience with real-world streaming analytics.
