Microsoft Fabric Business Intelligence Development Training Course

Course overview

Business intelligence teams are often expected to deliver trusted Power BI reporting while data is spread across operational systems, spreadsheets, lake storage and existing warehouses. Microsoft Fabric provides a unified SaaS platform for ingesting, engineering, modelling and serving data, but it introduces decisions about OneLake design, Lakehouse versus Warehouse patterns, semantic-model ownership, Direct Lake usage and workspace governance. This course equips practitioners to make those decisions and build BI solutions that remain supportable after the first dashboard is published.

Participants develop an end-to-end Fabric BI solution using Data Factory pipelines and Dataflows Gen2, Lakehouse and Warehouse storage, SQL and notebook transformations, and Power BI semantic models. They learn to structure a medallion-style data estate in OneLake, create star schemas, configure relationships and measures with DAX, apply row-level security, and select Import, DirectQuery or Direct Lake storage modes. The course also addresses refresh design, deployment pipelines, Git-enabled development, capacity-aware performance practices and monitoring.

Delivery combines instructor demonstrations with guided build exercises based on a realistic sales and operations reporting scenario. Each participant builds a governed Fabric workspace containing a data ingestion flow, curated Lakehouse or Warehouse tables, a reusable semantic model and an executive Power BI report. The final-day workshop turns this into an implementation plan, including workspace structure, security roles, release steps and operational ownership. Participants receive a completion certificate and leave with a practical reference architecture they can adapt for their organisation.

This intermediate course is designed for data professionals who already work with Power BI, SQL or data integration and now need to deliver business intelligence on Microsoft Fabric rather than treat it as a collection of isolated services.

Course objectives

By the end of this course, participants will be able to:

• Design a OneLake workspace and medallion architecture for a departmental or enterprise BI workload
• Build Data Factory pipelines and Dataflows Gen2 to ingest, cleanse and schedule source data
• Create Lakehouse tables and Warehouse schemas using Delta tables and T-SQL transformations
• Develop a star-schema Power BI semantic model with relationships, measures and calculation logic
• Write DAX measures for time intelligence, variance analysis and business KPI reporting
• Select and configure Import, DirectQuery and Direct Lake connectivity for defined reporting requirements
• Apply row-level security, workspace roles and sensitivity-label practices to a Fabric BI solution
• Produce a deployable Fabric BI implementation plan covering releases, monitoring and ownership

Benefits of attending

Target competencies

Who should attend

• Power BI Developers — who need to build governed semantic models and reports on Fabric data
• Business Intelligence Developers — who are moving ETL, warehousing and reporting workloads into Microsoft Fabric
• Data Analysts — who need reliable self-service reporting datasets rather than disconnected extracts
• Analytics Engineers — who design curated data layers and reusable metrics for business consumers
• Data Engineers — who need to connect Fabric ingestion and transformation patterns to BI delivery
• BI and Data Platform Leads — who must define workspace, security and deployment practices for Fabric adoption

Requirements and prerequisites

Participants should be comfortable creating Power BI reports, using tables and relationships, and writing basic SQL SELECT, JOIN and GROUP BY queries. Familiarity with Power Query, dimensional modelling concepts such as fact and dimension tables, and a current Power BI Service workflow is strongly recommended. Attendees should also understand their organisation's basic data-access process. Prior experience with Microsoft Fabric, Apache Spark, Python, Azure Synapse or data engineering notebooks is not required; these are introduced through guided exercises. Participants do not need to be database administrators or cloud architects.

Training methodology

The instructor uses a single business scenario throughout the week: integrating sales, customer and operational data into a managed reporting product. Short technical briefings establish each design decision, followed by hands-on work in Fabric workspaces using pipelines, Lakehouse or Warehouse objects, semantic models and Power BI reports. Participants compare alternative storage and connectivity choices, review each other's model designs, and troubleshoot refresh, security and performance issues. The final workshop converts the completed solution into an implementation plan for a participant-selected workplace use case.

Course outline

Day 1: Fabric BI architecture and OneLake foundations

• Microsoft Fabric workloads, capacities and tenant concepts
• OneLake architecture, domains and workspace organisation
• Lakehouse, Warehouse and Eventhouse use-case selection
• Medallion architecture with bronze, silver and gold data layers
• Fabric workspace roles and item-level access considerations
• Data source assessment for BI migration workloads
• Fabric BI solution architecture and data-flow mapping

Workshop: Design a OneLake-based workspace and medallion architecture for the course sales and operations reporting case, producing a source-to-report data-flow diagram.

Day 2: Data ingestion and curated data layers

• Data Factory pipeline activities and parameterised connections
• Dataflows Gen2 for low-code transformation and data ingestion
• Lakehouse files, tables and Delta Lake fundamentals
• Notebook-based transformations with Spark SQL
• Data quality checks and reconciliation controls
• Warehouse table creation and T-SQL transformation patterns
• Pipeline scheduling, dependencies and refresh failure handling

Workshop: Build and run an ingestion pipeline and Dataflow Gen2 process that loads raw sales data and produces validated curated tables.

Day 3: Semantic modelling and Power BI metrics

• Star-schema design for Fabric BI datasets
• Fact grain, conformed dimensions and surrogate key choices
• Power BI semantic model relationships and filter propagation
• DAX measures for revenue, margin, targets and variance
• Time-intelligence calculations using a marked date table
• Calculation groups and reusable business logic patterns
• Semantic model endorsement, documentation and ownership

Workshop: Create a reusable semantic model from curated Fabric tables and produce a validated KPI measure set for an executive reporting pack.

Day 4: Direct Lake performance, security and governed reporting

• Import, DirectQuery and Direct Lake storage-mode decisions
• Direct Lake behaviour, fallback considerations and model optimisation
• Power BI report design for executive and operational audiences
• Row-level security roles and test scenarios
• Microsoft Purview sensitivity labels and data protection considerations
• Refresh strategy, incremental refresh and semantic model maintenance
• Fabric capacity monitoring and performance troubleshooting

Workshop: Configure Direct Lake and security for the course semantic model, then diagnose a slow report page and document the recommended remediation.

Day 5: Deployment and Fabric BI operating model

• Development, test and production workspace topology
• Deployment pipelines and environment-specific configuration
• Git integration and source-control practices for Fabric items
• Release validation for pipelines, tables, models and reports
• Monitoring runs, refreshes and user-facing data incidents
• BI governance roles, support ownership and change control
• Fabric adoption roadmap and workload prioritisation

Workshop: Package the completed solution into a Fabric BI implementation plan containing release stages, owners, security controls, monitoring checks and a 90-day adoption backlog.

Tools & standards covered

Microsoft Fabric, Power BI Desktop, Microsoft Purview, GitHub

A typical training day

Live online deliveries follow the same structure in the East Africa Time zone, with shorter screen blocks and longer breaks.

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions