Microsoft Fabric: Monitor and Optimize an Analytics Solution

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Overview

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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 DAX or PySpark is helpful but not required. Foundational experience with cloud-based analytics platforms like Azure, Databricks, or Snowflake (optional but beneficial). Awareness of analytics workloads including dashboards, lakehouses, warehouses, and real-time streaming architectures. Interest in performance tuning, monitoring, and optimization of large-scale data workloads in modern analytics environments. By the End of This Course, You Will Be Able To: Monitor and troubleshoot data ingestion, transformation, and semantic models using Microsoft Fabric monitoring tools, alerts, and diagnostic views. Optimize performance for pipelines, notebooks, SQL endpoints, Eventstreams, Spark workloads, and semantic models across Fabric’s unified analytics engine. Identify, analyze, and resolve Fabric errors including T-SQL, Eventhouse, pipeline, and Dataflow errors using built-in debugging capabilities. Enhance enterprise-scale performance with best practices for query tuning, caching, incremental refresh, storage modes, and data optimization techniques. Operationalize and govern analytics solutions through proactive monitoring, alerting, and continuous performance improvement. Who Should Take This Course? This course is ideal for: Data Engineers working with performance-critical analytics workloads Fabric & Power BI Developers managing enterprise semantic models Data Architects & BI Engineers responsible for optimization at scale Analytics Administrators monitoring and maintaining Fabric environments Professionals preparing for Microsoft Fabric Associate or Expert certifications

Syllabus

Data Modeling and Optimization in Microsoft Fabric

Welcome to Week 1 of the Microsoft Fabric: Monitor and Optimize Analytics Solutions course. This week focuses on building high-performance semantic models that form the analytical backbone of dashboards, reports, and enterprise BI solutions. You’ll begin by choosing the right storage mode and understanding how semantic models are structured for speed and scalability. Next, you’ll build star schemas, implement relationships, and apply DAX calculations to support advanced analytical logic. We’ll also explore large-format dataset design, composite models, calculation groups, and field parameters - along with hands-on demos to help you optimize models for enterprise-scale workloads.

Monitoring, Optimization, and Troubleshooting in Microsoft Fabric

Welcome to Week 2! This week dives deep into monitoring, diagnosing, and optimizing semantic models and data processes within Microsoft Fabric. We’ll begin by monitoring data ingestion pipelines, transformation jobs, and workspace activities - followed by hands-on labs on configuring alerts, notifications, and activity monitoring. Next, you’ll explore semantic model tuning using query performance optimization, DAX improvements, and Fabric’s built-in optimization tools. We’ll also cover how to identify and resolve errors across pipelines, notebooks, dataflows, T-SQL operations, Eventstreams, and Eventhouse environments. By the end of this week, you’ll be equipped to maintain high-performance data systems and resolve operational issues across Fabric workloads.

Data Engineering and Performance Optimization in Microsoft Fabric

Welcome to Week 3 of the course. This week shifts the focus toward optimizing data engineering workloads and troubleshooting performance issues across Fabric’s multi-engine environment. You’ll begin by exploring lakehouses, Delta Lake tables, Spark workloads, Eventstream performance techniques, and data warehouse optimization. Through guided demos, you’ll learn how to tune storage, queries, Spark clusters, and ingestion pipelines for large-scale analytical workloads. Finally, we’ll walk through troubleshooting Azure Data Factory and Synapse pipelines, monitoring orchestration performance, and applying best practices for improving end-to-end data engineering efficiency in Fabric.