Fabric in 30 Days
30-day executive learning journey

Microsoft Fabric End-to-End in 30 Days

Learn the modern data platform through simple, visual, and enterprise-ready infographics.

Oneplatform
Onecopy of data
End-to-endanalytics for the AI era
Start Learning View 30-Day Roadmap

Why This Series

A concise path for teams that need clarity across Microsoft Fabric strategy, platform design, analytics delivery, and enterprise governance.

Understand Fabric architecture

See how workloads, storage, governance, and experiences connect into one analytics platform.

Build AI-ready data platforms

Move from scattered systems to trusted, reusable data products for analytics and AI scenarios.

Learn through visual infographics

Convert complex platform concepts into visual models that leaders and builders can discuss.

Apply enterprise best practices

Connect every topic to operating models, security, cost, governance, and adoption decisions.

Learning Outcomes

After 30 days, learners should be able to explain, design, and present Microsoft Fabric as an enterprise data architecture for analytics and AI adoption.

Explain Fabric clearly

Turn a broad platform into a simple executive-ready architecture story.

Map workloads to value

Connect OneLake, engineering, warehouse, Power BI, AI, and governance to business use cases.

Design AI-ready flows

Create data platform flows from sources to governed analytics, machine learning, and Copilot scenarios.

Discuss governance

Frame security, lineage, cost, ownership, and adoption as core architecture decisions.

Create reusable artifacts

Produce infographics, decision memos, use case flows, and workshop-ready discussion assets.

Who This Course Is For

This series is designed for mixed enterprise teams that need a shared Microsoft Fabric language across strategy, architecture, delivery, governance, analytics, and AI adoption.

Executives & Business Leaders

  • Need a clear platform narrative.
  • Want to understand AI-ready data foundations.
  • Need to sponsor adoption and funding decisions.
RecommendationFocus on business value, operating model, cost, governance, and adoption. Use assignments as executive discussion artifacts.
Days 1-51225-30

Enterprise & Solution Architects

  • Design the end-to-end architecture.
  • Map workloads to business capabilities.
  • Define integration, security, and governance patterns.
RecommendationComplete all 30 days, but spend extra time on flow diagrams, workload boundaries, OneLake, Purview, Azure integration, and operating model.
Days 1-1625-2830

Data Engineers & Platform Teams

  • Build ingestion, lakehouse, warehouse, and pipeline patterns.
  • Need reusable implementation standards.
  • Own reliability and performance.
RecommendationPrioritize data platform days, then connect them to governance and cost control so technical delivery scales responsibly.
Days 6-91519-23

BI Analysts & Analytics Leads

  • Build reports, semantic models, and metrics.
  • Need trusted definitions across teams.
  • Translate data products into decisions.
RecommendationFocus on Power BI in Fabric, semantic models, governance, real-time signals, and use cases. Turn assignments into report design briefs.
Days 1111317-1824

Data Scientists & AI Teams

  • Need governed data for ML and Copilot use cases.
  • Connect notebooks, models, and AI apps to business data.
  • Care about trustworthy AI outcomes.
RecommendationUse the series to connect AI work to data foundation, lineage, feature quality, semantic context, and enterprise governance.
Days 51014212730

Governance, Security & Compliance

  • Need lineage, access control, ownership, and policy clarity.
  • Support trusted self-service analytics.
  • Reduce risk in AI and data sharing.
RecommendationStart with architecture, then focus on governance, security, Purview, operating model, and enterprise adoption controls.
Days 111162528-29

Recommended learning path

For a complete learner, follow all 30 days in order. For a leadership audience, run the course as 6 weekly workshops: Foundation, Data Platform, Analytics, AI, Governance, and Enterprise Adoption. For delivery teams, use each assignment as a working artifact for your Fabric program.

Not ideal for

This is not a deep certification cram course or a pure hands-on lab series. It is best for learners who need architecture clarity, visual explanation, enterprise decision-making, and practical adoption patterns.

30-Day Learning Roadmap

Filter the path by category, then open each day for objectives, daily lesson plans, quizzes, assignments, and practical takeaways.

Day 1 30-day journey Day 30

Featured Infographic

Day 1 frames Microsoft Fabric as an integrated architecture, moving from source systems to governed analytics and AI outcomes.

Day 1

Microsoft Fabric Architecture

Data Sources to Ingestion to OneLake to Processing to Governance to Power BI and AI.

Data Sources

Business apps, files, events, databases, APIs, and operational platforms.

Ingestion

Pipelines, dataflows, shortcuts, streaming, and event-driven movement.

OneLake

A unified data lake foundation for open, reusable analytics data.

Processing

Lakehouse, warehouse, Spark, SQL, KQL, notebooks, and transformations.

Governance

Security, lineage, access, policies, domains, and stewardship practices.

Power BI / AI

Semantic models, reports, copilots, ML workflows, and intelligent apps.

Learning Method

Each day turns one complex topic into an executive-friendly learning asset with architectural context and practical direction.

Big Picture

Start with the business and architecture context before diving into platform mechanics.

Simple Visual

Use diagrams and short labels to make technical ideas easier to explain and remember.

Enterprise Insight

Connect each capability to governance, operating models, cost, security, and delivery scale.

Practical Takeaway

End with a concrete decision, design pattern, or discussion point for real projects.

Enterprise Use Cases

Microsoft Fabric becomes most valuable when platform capabilities are mapped to concrete data sources, governed workloads, analytics products, AI outcomes, and deployment flows.

Education

Fabric can unify student information, LMS activity, finance, admissions, attendance, and engagement data into a governed education analytics foundation.

Institutional Analytics
SIS
LMS
Finance
Data Factory
OneLake
Lakehouse
Retention dashboard
Student 360
AI early alerts
Fabric application
  • Build Student 360 data products in OneLake.
  • Use Power BI for retention, enrollment, and finance KPIs.
  • Use ML to identify at-risk learners earlier.
Governance focus
  • Protect student PII and academic records.
  • Separate faculty, advisor, finance, and leadership access.
  • Track lineage for official institutional metrics.
1. Connect SIS/LMS→2. Curate Student 360→3. Publish semantic model→4. Trigger intervention

Banking

Fabric supports governed banking analytics across risk, customer intelligence, fraud signals, regulatory reporting, and branch performance.

Risk & Compliance
Core banking
Cards
Fraud events
Eventstreams
KQL database
Warehouse
Risk reporting
Fraud monitoring
Customer insights
Fabric application
  • Stream transaction events for fraud monitoring.
  • Use Warehouse for controlled regulatory datasets.
  • Create executive risk dashboards in Power BI.
Governance focus
  • Apply strict access policies and sensitivity labels.
  • Maintain lineage for regulatory numbers.
  • Separate sandbox analytics from certified reporting.
1. Ingest core data→2. Detect anomalies→3. Certify risk data→4. Publish reports

Retail

Fabric connects sales, inventory, loyalty, digital behavior, supply chain, and promotion data to improve merchandising and customer decisions.

Customer 360
POS
E-commerce
Loyalty
OneLake
Medallion layers
Semantic model
Demand forecast
Inventory alerts
Campaign ROI
Fabric application
  • Unify customer, product, store, and channel data.
  • Use notebooks for demand forecasting features.
  • Use Data Activator for stockout and margin alerts.
Governance focus
  • Control access to customer and loyalty data.
  • Certify shared sales and margin metrics.
  • Keep campaign and finance definitions consistent.
1. Build Customer 360→2. Model demand→3. Monitor inventory→4. Activate actions

Manufacturing

Fabric can combine plant operations, IoT telemetry, quality, maintenance, ERP, and supply chain data for operational intelligence.

Plant Intelligence
Machines
Quality systems
ERP
Eventstreams
KQL
Lakehouse
OEE dashboard
Predictive maintenance
Quality alerts
Fabric application
  • Analyze telemetry and downtime events in near real time.
  • Join IoT signals with production orders and quality data.
  • Build maintenance models from historical failures.
Governance focus
  • Separate plant, engineering, and corporate views.
  • Define certified OEE and quality metrics.
  • Retain lineage between sensor data and decisions.
1. Stream telemetry→2. Correlate with ERP→3. Detect anomalies→4. Alert maintenance

Gaming

Fabric enables live operations analytics by streaming gameplay events, payments, user behavior, matchmaking, and campaign data into governed insight loops.

LiveOps
Gameplay events
Payments
Campaigns
Eventstreams
Real-Time Analytics
Data Science
Player segments
Churn prediction
Live balance alerts
Fabric application
  • Track live engagement, economy, and monetization signals.
  • Create cohorts for retention and personalization.
  • Use real-time alerts for outages or gameplay imbalance.
Governance focus
  • Protect player identities and payment signals.
  • Control experiment data access.
  • Define official retention and monetization metrics.
1. Capture events→2. Segment players→3. Predict churn→4. Adjust LiveOps

Healthcare

Fabric can bring clinical, claims, operations, scheduling, patient experience, and population health data together under strong governance.

Trusted Care Analytics
EHR
Claims
Operations
Data Factory
OneLake
Purview policies
Capacity planning
Care quality KPIs
Risk stratification
Fabric application
  • Build governed patient and operations analytics.
  • Use Power BI for quality, capacity, and finance views.
  • Use ML for readmission or risk stratification scenarios.
Governance focus
  • Protect PHI and sensitive clinical data.
  • Use role-based access for care, finance, and operations.
  • Track lineage for quality and compliance reporting.
1. Integrate EHR/claims→2. Curate patient data→3. Govern PHI→4. Publish care insights

Modernize Legacy Data Warehouse to Fabric

A practical reference model for organizations moving from older data warehouses to Microsoft Fabric without losing control of reporting, governance, or business continuity.

Legacy Estate

SQL Server, Synapse, Oracle, Teradata, ETL jobs, BI reports, and security models.

Assessment

Inventory schemas, data volumes, reports, dependencies, T-SQL gaps, and critical workloads.

Migration Path

Choose lift and shift, phased modernization, or hybrid transition based on risk and value.

OneLake Target

Land data in OneLake, organize with Lakehouse/Warehouse, and apply medallion layers.

Governance

Map roles, permissions, sensitivity, lineage, ownership, and Microsoft Entra identity.

Validate & Reroute

Run parallel checks, compare metrics, reconnect reports and pipelines, then retire legacy safely.

Path 1

Lift & Shift to Fabric Warehouse

Best when the existing model is already clean and the organization needs a lower-risk move.

  • Use Fabric Migration Assistant where supported.
  • Migrate schemas, tables, views, procedures, and data.
  • Run report comparison before switching consumers.
Path 2

Phased Modernization

Best when legacy ETL is complex, duplicated, expensive, or hard to govern.

  • Land source data into OneLake and Lakehouse.
  • Rebuild bronze, silver, and gold data products.
  • Publish certified semantic models for BI and AI.
Path 3

Hybrid Transition

Best when business continuity requires the old warehouse and Fabric to run side by side.

  • Use Data Factory, gateways, shortcuts, or copy jobs.
  • Move domain by domain instead of one big switch.
  • Retire legacy workloads only after validation.
Current Situation
Recommended Approach
Primary Focus
Clean star schema, few warehouses, strong time pressure
Lift & Shift to Fabric Warehouse
Speed and validation
Duplicated ETL, many data copies, weak lineage
Phased Modernization with OneLake and medallion architecture
Quality and reuse
On-prem systems, strict uptime, many dependent reports
Hybrid Transition with parallel run
Continuity and risk control
AI adoption depends on trusted enterprise data
Modernize semantic layer, governance, and curated data products
AI readiness
Migration checklist
  • Inventory tables, views, stored procedures, pipelines, reports, users, and dependencies.
  • Classify critical workloads, sensitive data, and business-owned metrics.
  • Assess T-SQL compatibility, data type mapping, security changes, and unsupported features.
  • Migrate or rebuild schema, data movement, transformation logic, and semantic models.
  • Validate row counts, report totals, performance, access, lineage, and cost impact.
  • Run parallel testing, reroute BI/ETL connections, and decommission legacy gradually.
Where this fits in the 30-day course

Use Day 29 as the main migration playbook. Connect it back to Day 6 OneLake, Day 8 Medallion Architecture, Day 12 Cost Optimization, Day 16 Governance & Security, Day 25 Purview, and Day 28 Operating Model.

Open Day 29 Migration Playbook

Choose Your Learning Path

This is a community learning project. Choose the path that fits how you want to learn, contribute, or enable a team.

Preview

Open Preview

Open access
  • Day 1 full lesson
  • 30-day roadmap
  • Sample quiz and assignment
  • Selected enterprise use cases
Start Preview
Community

Full Community Learning

Free for learners
  • All 30 detailed lessons
  • Quizzes and assignments
  • Progress tracking
  • Completion guidance
  • Feedback submission
Join Community Learning
Team

Team Enablement

Community supported
  • Architecture templates
  • Governance checklist
  • Use case canvas
  • Adoption roadmap
  • Team reporting and workshop option
Request / Contribute
This community project is not a paid course. It is a visual enterprise architecture playbook for professionals who want to learn, share, localize, and improve Microsoft Fabric adoption knowledge together.

Learning Analytics & Feedback

Use these signals to understand learner interest, identify the most valuable topics, and collect feedback for improving the series.

What to Track

  • Access and engagementPage views, section views, roadmap filters, day-card opens, and time-based interest signals.
  • Learning intentWhich days learners open most, which categories get filtered, and which topics trigger quiz attempts.
  • Understanding checkQuiz answer events help reveal concepts that need clearer explanation or better visuals.
  • Feedback loopComments, ratings, role, and selected day help prioritize updates by audience and topic.

Build Your AI-Ready Data Foundation

Microsoft Fabric is not just a data platform. It is the foundation for trusted analytics, intelligent automation, and AI-native enterprise.

Start Day 1