“The Microsoft Fabric implementation program gave our data engineering team a structured path from legacy pipelines to a modern lakehouse architecture.”
Enterprise Program Brief
Microsoft Fabric Data and AI Solution Engineering
Deliver a governed service-operations analytics solution with a bounded conversational analytics use case. Proposed instructor-led duration; delivery subject to environment and lab validation.
VNode ITeSPractitioner / specialist
Download Program Brief (PDF)Program Snapshot
Engagement scoped to cohort
Duration48 hours
FormatPrivate cohort
LevelPractitioner / specialist
Course CodeD01
- MCT-led delivery
- Private cohort / virtual ILT
- Typical response: 1 business day
Scoping call typically scheduled within one business day
VNode ITeS delivers Microsoft Fabric Data and AI Solution Engineering as an MCT-led VNode ITeS program for Data engineers, Analytics engineers. Typical duration is 48 hours (Private cohort). Delivery stays scoped to how your teams will actually use the platform after the room empties. Private cohorts can shift emphasis by role mix, workspace or repo constraints, and rollout timing.
Audience Profile
Built for these roles
Data engineers, Analytics engineers
- Tailored Team Delivery
Overview
Executive overview
Deliver a governed service-operations analytics solution with a bounded conversational analytics use case.
Environment and licensing
Fabric capacity and isolated workspace Author and consumer personas Source files and notebook runtime Data-agent labs: paid F2+ or qualifying P1+ capacity, tenant AI settings and source access as documented; cohort capacity sized separately Delivery depends on environment and lab validation. This outline does not include step-by-step lab instructions.
Prerequisites
- Fabric foundations and practical PySpark
- AI and Generative AI Foundations, or equivalent demonstrable experience
- Microsoft Fabric Engineering Foundations, or equivalent demonstrable experience
- PySpark for Data Engineering, or equivalent demonstrable experience
Program Outcomes
Capabilities your teams will gain
- Deliver Fabric source-to-insight and conversational analytics
- Reconcile data, measures and data-agent reference answers
- Prove persona access and bounded read-only AI consumption
- Demonstrate recovery and provide a data-product runbook
Enterprise Delivery
Private cohort delivery
VNode-designed, vendor-aligned instructor-led program. Scope role mix, delivery format, and rollout goals for your cohort.
Curriculum
Curriculum roadmap
Proposed instructor-led duration; delivery subject to environment and lab validation.
- Business requirements and data product architecture
- Incremental ingestion and orchestration
- Transformations and engineering quality
- Semantic and analytical consumption
- Governed conversational analytics
- Solution capstone and operations
1Module 1 · 8 hours
Business requirements and data product architecture
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Module 1 · 8 hours
Business requirements and data product architecture
Translate operational questions into an architecture and measurable acceptance tests.
KPIs and grain
- Define service metrics and analytical grain
- Translate business questions into reference queries
Source contracts
- Specify source schemas, owners and refresh needs
- Identify late and corrected records
Lakehouse versus warehouse
- Compare lakehouse and warehouse responsibilities
- Choose notebook and SQL processing boundaries
Ownership
- Assign data product and consumer owners
- Define measurable quality and access acceptance
Concept and design 4h · applied exercise 3h · review 1h. Included within module duration; no extra hours. Refine during delivery planning without silently changing published course hours.
Applied exercise: Business requirements and data product architecture
Translate operational questions into an architecture and measurable acceptance tests.
Expected output. Data product architecture and acceptance contract
Acceptance. The architecture supports agreed questions with clear contracts and ownership.
2Module 2 · 8 hours
Incremental ingestion and orchestration
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Module 2 · 8 hours
Incremental ingestion and orchestration
Build ingestion with controlled reruns and source reconciliation.
Pipelines
- Parameterise source connections and run dates
- Separate credentials from pipeline definition
Watermarks
- Capture incremental progress safely
- Design replay and backfill behaviour
Idempotency
- Prevent repeated loads from duplicating outputs
- Reconcile counts and keys between stages
Failure recovery
- Isolate transient source failures
- Document retry and recovery ownership
Concept and design 4h · applied exercise 3h · review 1h. Included within module duration; no extra hours. Refine during delivery planning without silently changing published course hours.
Applied exercise: Incremental ingestion and orchestration
Build ingestion with controlled reruns and source reconciliation.
Expected output. Reliable ingestion pipeline
Acceptance. The pipeline handles first load, increment, replay and controlled source failure.
3Module 3 · 8 hours
Transformations and engineering quality
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Module 3 · 8 hours
Transformations and engineering quality
Build curated tables and test late, duplicate and invalid records.
PySpark
- Transform raw records into curated tables
- Apply stable keys and business validation
Curated tables
- Use PySpark for appropriate transformations
- Compare reference SQL for reconciliation
Data quality
- Quarantine malformed and invalid records
- Make quality exceptions visible
Performance and cost awareness
- Inspect partition and execution choices
- Discuss performance using observed workloads
Concept and design 4h · applied exercise 3h · review 1h. Included within module duration; no extra hours. Refine during delivery planning without silently changing published course hours.
Applied exercise: Transformations and engineering quality
Build curated tables and test late, duplicate and invalid records.
Expected output. Validated transformation pipeline
Acceptance. Curated tables pass quality and reference checks, including late and duplicate inputs.
4Module 4 · 8 hours
Semantic and analytical consumption
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Module 4 · 8 hours
Semantic and analytical consumption
Publish agreed metrics and test results across consumer personas.
Semantic models
- Model facts, dimensions and relationships
- Define business measures and filter meaning
Measures
- Validate totals against reference queries
- Identify inconsistent or ambiguous metrics
Data access
- Test producer and consumer permissions
- Apply relevant semantic-model security
Lineage
- Trace published data back to source transformations
- Record owners of definitions and refreshes
Concept and design 4h · applied exercise 3h · review 1h. Included within module duration; no extra hours. Refine during delivery planning without silently changing published course hours.
Applied exercise: Semantic and analytical consumption
Publish agreed metrics and test results across consumer personas.
Expected output. Semantic product and access evidence
Acceptance. Published measures reconcile and persona access works through the actual consumption surface.
5Module 5 · 8 hours
Governed conversational analytics
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Module 5 · 8 hours
Governed conversational analytics
Configure a data agent and compare answers against reference queries.
Fabric data agent
- Configure a Fabric data agent over approved sources
- Keep source selection narrow and purposeful
Instructions and examples
- Write instructions and representative example questions
- Compare generated results with SQL/DAX reference evidence
SQL/DAX answer verification
- Test irrelevant, ambiguous and unsupported questions
- Inspect read-only and permission boundaries
Read-only boundaries
- Record answer quality and failure cases
- Separate standalone data-agent behaviour from preview cross-platform integration
Concept and design 4h · applied exercise 3h · review 1h. Included within module duration; no extra hours. Refine during delivery planning without silently changing published course hours.
Applied exercise: Governed conversational analytics
Configure a data agent and compare answers against reference queries.
Expected output. Conversational analytics evaluation record
Acceptance. Conversational results match reference evidence without exposing unauthorised data.
6Module 6 · 8 hours
Solution capstone and operations
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Module 6 · 8 hours
Solution capstone and operations
Demonstrate source-to-insight processing and recover from an injected failure.
End-to-end runs
- Run ingestion, transformation and consumption end to end
- Retain counts, quality and refresh evidence
Monitoring
- Diagnose an injected refresh or access failure
- Demonstrate recovery without corrupting totals
Quality gates
- Record cost and capacity assumptions
- Identify bottlenecks without promising production sizing
Runbook
- Present a runbook, owners and acceptance exceptions
- Defend the final data product against original business questions
Concept and design 2h · applied exercise 4h · review 2h. Included within module duration; no extra hours. Refine during delivery planning without silently changing published course hours.
Capstone: Solution capstone and operations
Demonstrate source-to-insight processing and recover from an injected failure.
Expected output. Assessed Fabric data and AI solution
Acceptance. The capstone demonstrates repeatable source-to-insight delivery and controlled recovery.
Capstone
Demonstrate source-to-insight processing and recover from an injected failure. Assessed Fabric data and AI solution Evidence of relevant access, quality and risk controls Demonstration and limitations record
Assessment
Scenario-based practical assessment Deliver Fabric source-to-insight and conversational analytics 30% Reconcile data, measures and data-agent reference answers 25% Prove persona access and bounded read-only AI consumption 25% Demonstrate recovery and provide a data-product runbook 20% Proposed VNode outline-level assessment; no vendor accreditation or completed lab validation implied.
Participant deliverables
- Assessed Fabric data and AI solution
- Evidence of relevant access, quality and risk controls
- Demonstration and limitations record
Scope boundaries
Step-by-step lab instructions; Runnable solution code; Packaged source datasets; Executed environment validation
Delivery options
Count teaching hours, not calendar days. Exclude breaks. Split into 4-hour or 8-hour teaching sessions; schedule complex project work in contiguous blocks.
Delivery Models
Delivery models
Virtual ILTOnsiteHybridExecutive WorkshopBootcampWeekend
Engagement Fit
Engagement fit
- Private cohort delivery
- Practitioner / specialist practitioner depth
- Business outcome alignment
Enterprise Customization
Enterprise customization
Tailor this program to your organization's priorities: Deliver a governed service-operations analytics solution with a bounded conversational analytics use case.
- Private cohort scheduling
- Equivalent-experience entry review
Resources
Program resources
Yes. Most enterprise clients prefer private delivery scoped to role mix, timezone, and rollout timeline. We align lab environments and scenarios to your tenant context where applicable.
Enterprise Proof
Trusted delivery outcomes
“We needed a partner who understood both the technical depth of Azure OpenAI and the governance requirements of an enterprise.”
Banking & Finance
Representative Enterprise Banking Team
The focus was not just on tooling knowledge, but on helping teams work from a shared operating model as they adopted a more modern data platform.
- Clearer platform operating model across teams
- Improved confidence in modern data stack adoption
Healthcare
Representative Healthcare Product Team
The engagement helped product and engineering stakeholders move from interest in AI to clearer implementation choices, security expectations, and prototyping discipline.
- Stronger alignment between product and engineering teams
- Improved clarity on prototype-to-production requirements
Delivery Capability
Enterprise-grade instruction
MCT-led delivery
Programs led by Microsoft Certified Trainer practitioners
Enterprise program oversight
Founder-led specialist delivery with structured rollout planning
Global delivery
APAC · EMEA · Americas · Virtual & Onsite
Implementation-focused
Hands-on labs aligned to production scenarios
