Course Introduction
Microsoft Fabric Lakehouse Analytics Training Course provides a comprehensive introduction to modern lakehouse-based analytics using Microsoft Fabric. The course explains how lakehouses combine the scalability of data lakes with analytical capabilities traditionally associated with data warehouses, enabling organizations to work with structured and unstructured data through integrated engineering and analytics experiences.
Participants explore how Microsoft Fabric Lakehouse works with OneLake, Delta tables, Spark, SQL, semantic models, and Power BI to create an integrated analytics environment. OneLake provides a unified organizational data lake, while Fabric lakehouses allow data engineering and analytics workloads to work with a shared copy of data rather than repeatedly moving or duplicating information.
The course develops practical skills for creating lakehouses, organizing data, loading and transforming datasets, querying information, and preparing analytical data for business users. Participants learn how to work across notebooks, SQL analytics endpoints, semantic models, and visualization tools while understanding how each experience contributes to an end-to-end lakehouse analytics workflow.
A major focus is analytical performance and the connection between lakehouse data and Power BI. Participants examine Direct Lake, a Microsoft Fabric storage mode designed to load data from Delta tables in OneLake for high-performance interactive analysis. The course explains how Direct Lake differs from Import and DirectQuery approaches and when each architecture may be appropriate.
The training also addresses emerging lakehouse analytics topics, including zero-copy data access, OneLake shortcuts, real-time analytics, AI-assisted analysis, data governance, security, semantic modeling, and optimization of Delta tables. OneLake shortcuts can connect data across supported internal and external locations without requiring traditional duplication, while current Fabric capabilities increasingly connect lakehouse analytics with broader enterprise data and AI workflows.
By the end of the course, participants will understand how to design and operate practical lakehouse analytics solutions in Microsoft Fabric. They will be able to connect engineering and analytical workflows, prepare trusted datasets, develop semantic models, query lakehouse data, and create business-focused insights. The course provides a strong foundation for professionals working with modern cloud analytics and Fabric-based data platforms.
Duration
5 days
Who Should Attend
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Data analysts seeking practical experience with Microsoft Fabric lakehouse analytics and modern analytical architectures.
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Data engineers who want to integrate engineering workflows with lakehouse-based reporting and analytical consumption.
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Business intelligence professionals interested in connecting Power BI and semantic models directly with Fabric lakehouse data.
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Power BI developers seeking practical knowledge of Direct Lake and modern Fabric analytical architectures.
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Database professionals transitioning from traditional warehouse environments toward cloud-based lakehouse solutions.
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Data scientists requiring scalable and accessible data environments for exploration, modeling, and advanced analytics.
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BI consultants responsible for designing modern data and reporting solutions for enterprise customers.
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Cloud professionals expanding their knowledge of Microsoft Fabric, OneLake, lakehouses, and integrated analytics.
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Data architects designing scalable enterprise data platforms that combine engineering, analytics, governance, and business intelligence.
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Reporting specialists who need stronger capabilities in lakehouse querying, semantic modeling, and analytical data preparation.
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IT managers and technology leaders overseeing cloud analytics modernization and data platform initiatives.
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Technical consultants implementing Fabric-based lakehouse solutions and helping organizations modernize their analytical environments.
Course Objectives
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Explain Microsoft Fabric lakehouse architecture and describe how OneLake, Delta tables, Spark, SQL, semantic models, and Power BI support integrated analytics.
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Create and configure lakehouses while organizing files, tables, schemas, and analytical structures for scalable enterprise data consumption.
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Apply OneLake concepts including centralized storage, shortcuts, data sharing, governance, and reduced data duplication across analytical environments.
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Load, transform, cleanse, and prepare structured and unstructured data for reliable lakehouse analytics and downstream business intelligence.
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Use Spark notebooks and SQL analytics endpoints to explore, query, transform, validate, and analyze lakehouse datasets effectively.
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Design analytical data structures that support efficient queries, reusable datasets, semantic modeling, reporting, and organizational decision-making.
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Develop Power BI semantic models and reports using Fabric lakehouse data while applying appropriate modeling and visualization techniques.
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Understand Direct Lake architecture, performance considerations, security implications, and differences between Direct Lake, Import, and DirectQuery approaches.
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Apply data governance, security, quality, lineage, and access-control principles to support trusted and responsibly managed lakehouse analytics.
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Evaluate emerging lakehouse technologies involving AI-assisted analytics, real-time intelligence, zero-copy integration, automation, and intelligent data discovery.
Comprehensive Course Outline
Module 1: Microsoft Fabric Lakehouse Foundations
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Understanding the role of lakehouse architecture in modern analytics and how Fabric combines data lake scalability with analytical querying.
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Exploring Microsoft Fabric architecture and how lakehouses interact with OneLake, Spark, SQL, semantic models, Power BI, and other workloads.
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Comparing traditional data warehouses, data lakes, and lakehouse architectures based on storage, processing, governance, scalability, and analytical requirements.
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Identifying appropriate lakehouse use cases for business intelligence, data engineering, data science, reporting, AI, and enterprise analytical applications.
Module 2: OneLake and Lakehouse Data Architecture
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Understanding OneLake as a unified organizational data foundation and its role in providing shared access to Fabric analytical workloads.
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Creating lakehouses and organizing data using appropriate files, folders, tables, schemas, and logical structures for analytical consumption.
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Exploring OneLake shortcuts and zero-copy data access approaches for connecting data across supported internal and external storage locations.
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Designing scalable lakehouse architectures that minimize unnecessary duplication while supporting governed access across teams and analytical workloads.
Module 3: Data Ingestion and Lakehouse Preparation
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Understanding batch and incremental ingestion approaches for bringing enterprise databases, files, applications, APIs, and other sources into lakehouse environments.
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Using Fabric data pipelines and transformation capabilities to automate data movement, cleansing, preparation, and loading into analytical lakehouse structures.
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Applying data validation, schema handling, deduplication, enrichment, and transformation techniques to create trusted analytical datasets.
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Exploring emerging ingestion patterns involving automated pipelines, metadata-driven workflows, changing schemas, hybrid environments, and continuously updated data.
Module 4: Delta Lake and Lakehouse Table Management
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Understanding Delta Lake concepts including transactional consistency, schema enforcement, versioning, time travel, and reliable analytical data management.
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Creating and managing lakehouse tables while applying appropriate structures for performance, maintainability, governance, and downstream analytical workloads.
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Exploring table optimization concepts including file organization, partitioning, statistics, and maintenance practices for improving analytical performance.
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Examining current lakehouse interoperability developments involving Delta and Iceberg table formats and broader open-format data ecosystem requirements.
Module 5: Spark and SQL Lakehouse Analytics
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Using Fabric notebooks and Apache Spark to explore, transform, aggregate, and analyze large-scale datasets stored within lakehouse environments.
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Applying PySpark and Spark SQL techniques for data preparation, analytical exploration, joins, filtering, aggregation, and advanced transformation requirements.
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Using the lakehouse SQL analytics endpoint to query Delta tables with T-SQL and support analyst-friendly access to structured analytical information.
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Comparing Spark and SQL analytical experiences and selecting appropriate tools according to data engineering, exploration, performance, and business requirements.
Module 6: Semantic Models and Direct Lake Analytics
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Understanding semantic models and their role in translating lakehouse datasets into consistent, reusable, business-oriented analytical structures.
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Creating Direct Lake semantic models that connect analytical experiences with Delta tables stored in OneLake for interactive business intelligence workloads.
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Comparing Direct Lake with Import and DirectQuery storage modes and evaluating their respective architecture, performance, refresh, and connectivity considerations.
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Applying Direct Lake design considerations including table optimization, data access requirements, model structure, capacity considerations, and analytical workload characteristics.
Module 7: Power BI Reporting from Lakehouse Data
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Connecting Power BI analytical experiences with Fabric lakehouse data and designing reports that communicate business information clearly and effectively.
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Developing measures, relationships, calculated logic, filtering, drill-through experiences, and interactive visualizations within lakehouse-based semantic models.
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Applying analytical storytelling and visualization principles to convert prepared lakehouse data into useful operational and strategic business insights.
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Exploring emerging AI-assisted reporting capabilities, natural-language analytics, automated insights, and intelligent interactions across modern business intelligence environments.
Module 8: Performance Optimization and Analytical Efficiency
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Identifying factors that influence lakehouse query performance, including table structure, file organization, data volume, model design, and analytical workload patterns.
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Applying Delta table optimization practices that support efficient Direct Lake consumption and responsive interactive analytical experiences.
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Optimizing Spark notebooks, SQL queries, semantic models, pipelines, and analytical reports to improve resource utilization and user experience.
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Evaluating performance trade-offs involving storage modes, data granularity, refresh approaches, capacity resources, query patterns, and growing enterprise data volumes.
Module 9: Governance, Security, and Trusted Analytics
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Understanding lakehouse governance principles covering data ownership, discovery, classification, lineage, quality, access, and responsible analytical use.
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Applying OneLake security concepts to control access to lakehouse data and understand how permissions influence analytical consumption.
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Exploring Direct Lake security behavior, effective identities, OneLake permissions, and role-based access considerations for governed analytical solutions.
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Addressing emerging governance challenges involving sensitive information, privacy, regulatory requirements, AI-enabled analytics, cross-workspace sharing, and external data integration.
Module 10: Emerging Lakehouse Analytics and AI
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Exploring real-time intelligence capabilities and how lakehouse environments can increasingly support continuously updated operational and analytical information.
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Examining AI-assisted analytics, intelligent data discovery, machine learning integration, and emerging technologies that extend traditional lakehouse use cases.
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Understanding zero-copy and shortcut-based data integration approaches for connecting broader enterprise data ecosystems without unnecessary data duplication.
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Developing an end-to-end lakehouse analytics solution integrating OneLake, ingestion, Delta tables, Spark, SQL, semantic modeling, Power BI, governance, and emerging AI capabilities.
Training Approach
This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.
Tailor-Made Course
This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808
Training Venue
The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.
Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant
Certification
Participants will be issued with Upskill certificate upon completion of this course.
Airport Pickup and Accommodation
Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808
Terms of Payment:
Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.
