Air Quality Real Time Analytics in Microsoft Fabric

Build a Real-Time Air Quality Analytics Solution with Microsoft Fabric, Eventstream, KQL, and Power BI

What you’ll learn
✓ Build an end-to-end air quality analytics solution in Microsoft Fabric using real-world OpenAQ data.
✓ Ingest near real-time sensor measurements and metadata through Eventstream and load them into Eventhouse / KQL Database.
✓ Design and implement a medallion architecture (Bronze, Silver, Fact/Calc layers) for streaming environmental data.
✓ Create and manage dimension and fact tables for air quality reporting, including location, pollutant, and daily sensor fact models.
✓ Write KQL scripts for transformation and feature engineering, including pollutant categorization, daily change, and moving averages.
✓ Build a semantic model and reporting layer to analyze air quality trends and pollutant behavior in Power BI.

Requirements
● Basic understanding of Microsoft Fabric concepts is helpful, but I will explain the project flow step by step.
● Familiarity with data analytics or data engineering fundamentals such as tables, transformations, and basic modeling will make learning easier.
● A Microsoft Fabric-enabled workspace is recommended so you can follow the hands-on exercises.
● Basic exposure to KQL or SQL is useful, though the required queries will be covered in the course.
● No prior experience with air quality data is required — the domain concepts and OpenAQ API usage will be introduced as part of the project.
● A laptop or desktop with internet access is enough to complete the course labs and exercises.

Description
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.

Who this course is for
■ This course is for data professionals, Microsoft Fabric learners, and analytics enthusiasts who want to build a real-world end-to-end project using near real-time environmental data.
■ Data Engineers and Analytics Engineers who want hands-on experience with Eventstream, Eventhouse, KQL, and medallion architecture in Fabric
■ Power BI and Fabric professionals looking to connect real-time ingestion with semantic modeling and reporting
■ Students and career switchers who want a portfolio-ready project in a meaningful domain like air quality analytics
■ Solution architects and technical consultants who want to understand how Microsoft Fabric can be used for streaming and operational analytics scenarios
■ Anyone preparing for practical Fabric projects and wanting to move beyond theory into implementation

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