Advanced Analytics & AI Optimization with Microsoft Fabric

Microsoft via Coursera

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Paid Certificate Available
Duration & workload

22 hours 47 minutes
Sessions

On-Demand
Subtitles

English

Found in

Part of

Microsoft Data Architecture for Modern Data Stacks

Overview

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This course will equip you with the skills to build high-performance, intelligent data solutions. You will gain hands-on experience by building robust semantic models in Power BI, implementing the groundbreaking DirectLake mode for lightning-fast analytics, and leveraging the power of Copilot in Fabric to dramatically boost your productivity. The course also covers making critical decisions on connection modes and semantic models to optimize performance and cost. By the end of this course, you will be able to analyze an organization's needs and recommend a comprehensive optimization strategy that improves performance while managing costs.

Syllabus

Power BI semantic models

Build the foundation for enterprise-wide self-service analytics by mastering Power BI semantic model development in Microsoft Fabric. You'll design reusable semantic models that connect to Lakehouse data, create sophisticated business calculations using DAX programming language, implement proper table relationships, and optimize models for performance. Through hands-on exercises and guided demonstrations, you'll learn how well-structured semantic models enable consistent business logic, accurate cross-filtering, and scalable analytics performance. This module provides the expertise needed to create semantic models that serve as a reliable foundation for all downstream analytics and reporting. Note:Unlike traditional import mode, DirectLake creates a direct connection to your Lakehouse data. When column names change, tables are restructured, or data types are modified, your Power BI reports can fail immediately. Best Practice: Define a version-controlled abstraction layer (views or gold tables) before connecting Power BI to DirectLake mode. This prevents schema changes from breaking visuals. Quick Recovery Tips: Keep a backup semantic model (.pbix) that you can quickly republish Use consistent naming conventions to minimize future conflicts Consider using views in your lakehouse for an abstraction layer Set up alerts to monitor report failures after schema changes

DirectLake (real-time) and Incremental refresh (partitioned batch)

Implement advanced data connectivity and refresh strategies to maximize performance while minimizing data duplication in Microsoft Fabric. You'll explore DirectLake mode for real-time analytics on Lakehouse data, compare its benefits to traditional import approaches, design effective partitioning strategies for large datasets, and configure incremental refresh policies that optimize update processes. Through practical exercises and performance comparisons, you'll develop the skills to implement data connections that balance query performance with freshness requirements. This module equips you with techniques to handle enterprise-scale datasets efficiently while maintaining responsive analytics experiences. Note: For optimization, large datasets may exceed trial compute credits during refresh operations. Start with a subset of data and review Incremental Refresh Best Practices before scaling up.

Embedded analytics and AI in Power BI

Extend the reach and intelligence of your Power BI solutions by implementing embedded analytics and AI-powered visualizations. You'll learn to securely publish and share Power BI reports, embed interactive dashboards into applications and portals, implement AI visuals that automatically discover patterns in your data, and configure natural language capabilities that enable conversational analytics. Through hands-on implementation exercises, you'll create compelling analytics experiences that integrate seamlessly with business applications while leveraging artificial intelligence to enhance insight discovery. This module bridges the gap between standard reporting and intelligent, accessible analytics.

AI integration with Copilot and data agents

Accelerate data development workflows through AI-powered assistance and automation in Microsoft Fabric. You'll harness Copilot's capabilities to build data pipelines using natural language, generate optimized SQL queries, create documentation summaries, configure Data Agents for automated tasks, and implement lightweight automation with Copilot Studio. Through guided explorations and practical exercises, you'll experience how AI assistance transforms data development productivity while maintaining quality and best practices. This module demonstrates how conversational AI can dramatically reduce development time while enabling broader participation in data engineering activities. Important Safety Note: Always review generated steps before execution, never paste secrets or sensitive information into AI prompts, and verify the preview/GA status of Copilot features in your tenant before implementation.

Architecture optimization and cost control

Master advanced architectural design and optimization techniques that ensure your Microsoft Fabric implementation is performant, cost-effective, and future-ready. You'll design mesh architectures with decentralized data domains, apply systematic performance optimization through caching, partitioning, and indexing, implement comprehensive cost monitoring and control strategies, and explore machine learning integration options. Throughout the module, you'll use a decision log template to capture cost/performance trade-offs for each architectural choice (e.g., DirectLake vs Import, Lakehouse vs Warehouse) to build systematic decision-making skills. Through architecture workshops and optimization exercises, you'll develop the skills to design, optimize, and govern enterprise-scale data platforms. This module provides the expertise needed to create sustainable, high-performance data architectures that balance business needs with technical and financial considerations.