Course Introduction
Data Analytics with Microsoft Fabric Training Course provides a comprehensive and practical introduction to modern data analytics using Microsoft Fabric. The course explores how organizations can bring together data integration, engineering, warehousing, analytics, business intelligence, and AI capabilities within a unified cloud-based platform. Participants gain an understanding of how Fabric supports the complete data lifecycle.
The course introduces participants to the Microsoft Fabric ecosystem and its major workloads, including OneLake, Data Factory, Data Engineering, Data Science, Data Warehouse, Real-Time Intelligence, and Power BI. Through structured learning, participants discover how these integrated services work together to reduce data silos, simplify analytics architectures, and create reliable data solutions for organizational decision-making.
Participants will develop practical knowledge of collecting, transforming, storing, modeling, analyzing, and visualizing data within Microsoft Fabric. The training emphasizes modern analytics practices, including lakehouse architecture, semantic modeling, data pipelines, SQL analytics, notebooks, real-time analytics, and business intelligence. These capabilities help learners understand how raw data can be transformed into meaningful and actionable insights.
The course also addresses emerging developments in data analytics, including artificial intelligence, machine learning, real-time data processing, automated data transformation, governance, and responsible use of enterprise data. Participants explore how Microsoft Fabric can support increasingly sophisticated analytics requirements while maintaining attention to data quality, security, compliance, scalability, and operational efficiency across modern data environments.
Designed for professionals with different levels of analytics and data experience, the course combines conceptual understanding with practical application. Participants examine realistic business scenarios and learn how to select appropriate Fabric workloads for different analytical requirements. The learning approach supports professionals who want to strengthen their technical capabilities, improve analytics workflows, and contribute more effectively to data-driven projects.
By the end of the course, participants will have a structured understanding of how Microsoft Fabric can be used to build modern end-to-end analytics solutions. They will be better prepared to work with enterprise data, develop analytical assets, create meaningful reports, support real-time decision-making, and apply governance principles. The course also provides a foundation for continued development in Microsoft Fabric, Power BI, data engineering, data science, and cloud analytics.
Duration
5 days
Who Should Attend
-
Data analysts seeking practical skills in Microsoft Fabric and modern cloud analytics environments.
-
Business intelligence professionals responsible for transforming organizational data into actionable insights.
-
Data engineers who want to develop expertise in Fabric lakehouse, pipelines, warehouses, and OneLake.
-
Power BI professionals interested in extending their analytics capabilities through Microsoft Fabric.
-
Database professionals transitioning from traditional platforms to integrated cloud data architectures.
-
Data scientists who need unified access to enterprise datasets for analytics and machine learning workflows.
-
Business users and decision-makers who want to understand modern data analytics capabilities and applications.
-
IT professionals responsible for implementing, supporting, or administering enterprise analytics platforms.
-
Cloud professionals expanding their knowledge of Microsoft's modern data and analytics ecosystem.
-
Project managers and technology leaders overseeing data transformation and business intelligence initiatives.
-
Reporting specialists seeking to improve data modeling, visualization, and enterprise reporting capabilities.
-
Consultants and solution architects designing scalable analytics solutions for organizations and clients.
Course Objectives
-
Explain the architecture, capabilities, and major workloads of Microsoft Fabric and how they support an integrated end-to-end data analytics environment.
-
Configure and work with OneLake to understand centralized data storage, data organization, accessibility, and integration across Microsoft Fabric workloads.
-
Design and implement lakehouse solutions that combine scalable data storage, engineering, transformation, SQL analytics, and business intelligence requirements.
-
Build and manage data ingestion and transformation pipelines using Fabric Data Factory while applying appropriate approaches for diverse enterprise data sources.
-
Develop analytical solutions using notebooks, SQL, data engineering techniques, and Fabric tools to prepare reliable datasets for advanced analysis.
-
Create effective data models and semantic models that support consistent reporting, business calculations, analytical performance, and reusable organizational insights.
-
Develop Power BI reports and dashboards connected to Fabric data assets while applying visualization principles that communicate business information clearly.
-
Explore real-time analytics capabilities for processing streaming and event-driven data and identify appropriate use cases for operational decision-making.
-
Apply data governance, security, quality, lineage, and compliance concepts to help organizations manage analytics environments responsibly and effectively.
-
Evaluate emerging Microsoft Fabric capabilities involving artificial intelligence, machine learning, automation, and intelligent analytics for modern enterprise data initiatives.
Comprehensive Course Outline
Module 1: Microsoft Fabric Architecture and Modern Analytics
-
Understanding Microsoft Fabric architecture, core workloads, unified experiences, and the role of an integrated analytics platform.
-
Exploring OneLake as a centralized organizational data foundation and understanding how Fabric workloads interact with shared data.
-
Examining modern data analytics architectures, including lakehouse, warehouse, semantic models, pipelines, and business intelligence components.
-
Identifying appropriate Microsoft Fabric workloads for common enterprise analytics, reporting, engineering, data science, and real-time use cases.
Module 2: OneLake, Lakehouse, and Data Storage
-
Creating and organizing Fabric lakehouses while understanding tables, files, folders, schemas, and analytical storage considerations.
-
Exploring OneLake data organization, shortcuts, data accessibility, and methods for reducing unnecessary data duplication across environments.
-
Understanding Delta Lake concepts, table structures, transactional capabilities, and their importance for reliable analytical workloads.
-
Applying practical approaches for designing scalable lakehouse storage structures that support engineering, analytics, and reporting requirements.
Module 3: Data Ingestion and Fabric Data Factory
-
Understanding data ingestion patterns and selecting suitable approaches for bringing structured and unstructured information into Fabric.
-
Designing data pipelines that connect diverse enterprise sources while supporting repeatable, maintainable, and scalable data movement.
-
Applying transformation activities, parameters, scheduling, monitoring, and error-handling techniques within Fabric data pipelines.
-
Exploring emerging data integration requirements involving APIs, cloud applications, automated ingestion, hybrid environments, and continuously changing data.
Module 4: Data Engineering with Microsoft Fabric
-
Using Fabric notebooks and engineering tools to explore, transform, cleanse, enrich, and prepare datasets for analytical consumption.
-
Applying PySpark and SQL techniques to process large datasets while understanding practical considerations for performance and scalability.
-
Developing reusable data transformation workflows that improve consistency, reliability, maintainability, and operational efficiency.
-
Examining emerging data engineering practices involving automation, distributed processing, metadata-driven workflows, and intelligent data preparation.
Module 5: Data Warehouse and SQL Analytics
-
Understanding Fabric Data Warehouse architecture and its role in structured enterprise analytics, reporting, and business intelligence solutions.
-
Creating tables, views, relationships, queries, and analytical structures using SQL within a modern cloud-based warehouse environment.
-
Applying dimensional modeling concepts to organize business data for efficient querying, reporting, aggregation, and analytical consumption.
-
Comparing warehouse and lakehouse approaches and identifying suitable architectures based on workload, governance, performance, and business requirements.
Module 6: Semantic Models and Power BI Analytics
-
Understanding semantic models and their role in providing consistent business definitions, calculations, relationships, and reusable analytical structures.
-
Developing Power BI reports and dashboards that transform Fabric datasets into clear, interactive, and decision-oriented business information.
-
Applying data modeling, measures, calculated logic, filtering, drill-through, and visualization techniques to improve analytical usability.
-
Exploring emerging self-service analytics practices involving natural language interaction, AI-assisted insights, automated reporting, and intelligent visualization.
Module 7: Real-Time Intelligence and Streaming Analytics
-
Understanding real-time intelligence concepts and identifying business situations where continuously updated data can improve operational awareness.
-
Exploring event streams, real-time data processing, querying, monitoring, and analytical patterns for rapidly changing information environments.
-
Designing real-time analytics scenarios for operational dashboards, alerts, IoT information, application events, and time-sensitive business processes.
-
Examining emerging challenges involving streaming data quality, latency, event volume, real-time governance, and integration with traditional analytics.
Module 8: Data Science, Machine Learning, and AI
-
Exploring how Microsoft Fabric supports data science workflows, including experimentation, data preparation, model development, and analytical collaboration.
-
Understanding practical machine learning workflows that use governed enterprise data to develop predictive and classification-oriented analytical solutions.
-
Examining generative AI and intelligent analytics capabilities that can accelerate data exploration, insight generation, development, and business productivity.
-
Discussing responsible AI considerations including data quality, transparency, privacy, model limitations, human oversight, and appropriate enterprise adoption.
Module 9: Governance, Security, and Data Management
-
Understanding Fabric governance principles covering data discovery, ownership, classification, lineage, quality, access, and responsible data usage.
-
Applying security concepts such as permissions, roles, workspace management, authentication, and controlled access to analytical resources.
-
Exploring data lineage and monitoring capabilities that help organizations understand data movement, dependencies, transformations, and analytical consumption.
-
Addressing emerging governance issues involving artificial intelligence, privacy regulations, sensitive information, cross-platform integration, and responsible data management.
Module 10: Performance, Deployment, and Emerging Fabric Trends
-
Applying performance optimization principles to improve queries, data models, pipelines, reports, storage structures, and overall analytical workloads.
-
Understanding deployment and lifecycle management approaches for moving Fabric solutions between development, testing, and production environments.
-
Exploring emerging Microsoft Fabric capabilities, intelligent automation, AI-enhanced analytics, real-time processing, and evolving cloud data architecture patterns.
-
Developing an end-to-end Fabric analytics solution that integrates ingestion, engineering, storage, modeling, visualization, governance, and business insight generation.
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.
