Advanced Analytics & AI Optimization with Microsoft Fabric

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.

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.

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.

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.