Microsoft Fabric: Monitor and Optimize an Analytics Solution

Welcome to Microsoft Fabric: Monitor and Optimize Analytics Solutions, an advanced and hands-on course designed for data professionals who want to master monitoring, performance tuning, and troubleshooting within Microsoft Fabric’s unified analytics platform. This course teaches you how to ensure that Fabric workloads remain reliable, performant, and optimized for enterprise-scale analytics.

This advanced course is designed for data engineers and analytics professionals who want to master performance optimization, monitoring, and troubleshooting within Microsoft Fabric. Throughout the course, you’ll explore how to design scalable semantic models, optimize enterprise-scale workloads, diagnose ingestion and transformation issues, and accelerate performance for lakehouses, Spark environments, event streams, and data warehouses.

With 3+ hours of focused video content, the course blends conceptual understanding with real-world demonstrations inside Fabric. You will learn how to tune DAX, improve query performance, optimize pipelines, resolve Eventstream/Eventhouse errors, and manage large-scale data storage. Each module includes interactive quizzes and in-video checkpoints to reinforce learning.

Enroll in Microsoft Fabric: Optimize, Monitor, and Troubleshoot Data Solutions to gain the skills needed to improve system reliability, maximize performance efficiency, and support enterprise-grade data workloads in Microsoft Fabric.

Course Modules

Module 1: Data Modeling and Optimization in Microsoft Fabric:

Module 2: Monitoring, Optimization, and Troubleshooting in Microsoft Fabric

Module 3: Data Engineering and Performance Optimization in Microsoft Fabric

Recommended Background

A basic understanding of Microsoft Fabric components such as Lakehouses, Warehouses, Pipelines, and Eventstreams.

Familiarity with core data engineering concepts - data ingestion, transformation, modeling, and analytics workflows.

Working knowledge of SQL or experience with Power BI; exposure to DA...