Start with the right foundation.
- Power BI developers moving into Fabric
- Analytics engineers
- Data analysts responsible for semantic models
- BI teams modernizing warehouse and reporting workflows
MICROSOFT FABRIC ANALYTICS PATH
Microsoft Fabric and Power BI Analytics Engineering
Create governed analytics solutions with OneLake, lakehouses, warehouses, semantic models, DAX, Power BI and deployment lifecycle controls.
Learn how data becomes a trusted business decision—from ingestion and medallion design to dimensional models, semantic measures, report performance, security and controlled deployment across Microsoft Fabric and Power BI.
Recommended prerequisites.
- Comfort with SQL
- Basic Power BI report experience
- Foundational dimensional-modeling concepts
ROLE-BASED OUTCOMES
Learn to make and defend real implementation decisions.
The goal is not to memorize product menus. Each outcome connects architecture, implementation and operational evidence.
Choose between lakehouse, warehouse and real-time patterns
Build dimensional models and reusable semantic measures
Design secure Power BI experiences that remain performant
Operate Fabric workspaces with lineage, deployment and monitoring evidence
Tools and platformsMicrosoft FabricOneLakeLakehouseFabric WarehouseData FactoryPower BIDAXDeployment Pipelines
DETAILED SYLLABUS
One connected path from concepts to production evidence.
Every module includes a practical deliverable so you can explain what you built, why you chose it and how you verified it.
Fabric architecture and workload choices
Match business latency, skill and governance needs to the right Fabric workload.
- OneLake
- Lakehouse versus warehouse
- Shortcuts
- Capacity and workspaces
Hands-on lab
Create a decision matrix for batch analytics, governed BI and near-real-time reporting.
Ingestion and medallion design
Land and transform data while preserving traceability from source to curated product.
- Pipelines and dataflows
- Notebooks
- Bronze, silver and gold
- Incremental processing
Hands-on lab
Build a pipeline that lands sales files and produces validated silver and gold tables.
Dimensional and semantic modeling
Create models that make business meaning explicit and keep filter behavior predictable.
- Star schemas
- Relationships
- Direct Lake
- Semantic model design
Hands-on lab
Design a sales star schema and validate relationship direction and grain with business questions.
DAX and business measures
Write measures that preserve filter context and communicate their assumptions.
- CALCULATE
- Filter context
- Time intelligence
- Measure organization
Hands-on lab
Build revenue, margin and period-comparison measures with tests for totals and edge cases.
Report experience, security and performance
Balance usability with row-level security and measurable performance.
- Report design
- RLS and OLS
- Performance Analyzer
- Accessibility
Hands-on lab
Create an executive report, test two security roles and remove one measured performance bottleneck.
Lifecycle, governance and monitoring
Promote analytics changes safely while retaining ownership and operational visibility.
- Git and deployment pipelines
- Lineage
- Endorsement
- Monitoring hub
Hands-on lab
Promote a model and report through environments with validation, rollback notes and ownership metadata.
CAPSTONE WORK
Projects designed for explanation, review and improvement.
A useful portfolio shows decisions and evidence, not screenshots alone.
Executive sales analytics
Create a governed sales solution from source files to an accessible Power BI report.
Evidence to retain- Medallion flow
- Star schema
- Measure tests
- Security validation
Direct Lake performance review
Compare architecture and report choices for a growing semantic model.
Evidence to retain- Decision record
- Performance baseline
- Model changes
- Capacity considerations
Analytics release pipeline
Implement an environment promotion process for a model and report.
Evidence to retain- Release checklist
- Automated checks
- Lineage review
- Rollback procedure
CONTINUE WITH PRIMARY SOURCES
Use current official documentation alongside guided practice.
Technology changes. These primary references help you verify current features and continue learning after the course. ITCertPath is an independent training brand and is not endorsed by the listed vendors.
COURSE QUESTIONS
What to confirm before you enrol.
Ask for a written syllabus, delivery plan, trainer profile, lab arrangement, fee breakdown and cancellation terms.
Does this prepare me for DP-600 or DP-700?
It builds practical Fabric and Power BI analytics skills relevant to both roles, with stronger emphasis on analytics engineering. Certification candidates should compare the current official DP-600 or DP-700 study guide with the outline because objectives can change.
Will I learn Power BI as well as Fabric?
Yes. The path connects Fabric ingestion and storage to semantic models, DAX, report design, security, performance and lifecycle management.
Is a Fabric capacity included?
The page does not promise a hosted lab or paid capacity. Confirm the exact lab tenant, licensing arrangement and access duration before enrolling.
PLAN YOUR NEXT SKILL
Discuss Fabric + Power BI training with an advisor.
Tell us your experience, target role, time zone and preferred learning format. We will share the current options without promising a batch that has not been confirmed.