“The Microsoft Fabric implementation program gave our data engineering team a structured path from legacy pipelines to a modern lakehouse architecture.”
Enterprise Program Brief
Databricks Data and AI Solution Engineering
Deliver a governed lakehouse data product with a bounded AI consumption interface. 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 CodeD02
- MCT-led delivery
- Private cohort / virtual ILT
- Typical response: 1 business day
Scoping call typically scheduled within one business day
VNode ITeS delivers Databricks Data and AI Solution Engineering as an MCT-led VNode ITeS program for Data engineers, Solution 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, Solution engineers
- Tailored Team Delivery
Overview
Executive overview
Deliver a governed lakehouse data product with a bounded AI consumption interface.
Environment and licensing
Azure Databricks workspace with Unity Catalog Appropriate compute/SQL and scoped catalog/storage access AI courses: approved model access, retrieval service and MLflow Deployment courses: supported serverless/Apps/runtime access and service identity Delivery depends on environment and lab validation. This outline does not include step-by-step lab instructions.
Prerequisites
- Databricks engineering competence
- AI and Generative AI Foundations, or equivalent demonstrable experience
- Databricks Engineering Foundations, or equivalent demonstrable experience
Program Outcomes
Capabilities your teams will gain
- Deliver governed lakehouse ingestion, serving and bounded AI access
- Prove quality, history, replay and reference-query correctness
- Restrict runtime data privileges and prohibit write actions
- Present operational evidence and accountable product ownership
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.
- Use-case and lakehouse design
- Incremental ingestion
- Transformation and pipeline engineering
- Governance and analytical serving
- Bounded AI consumption
- Operational capstone
1Module 1 · 8 hours
Use-case and lakehouse design
+
Module 1 · 8 hours
Use-case and lakehouse design
Define a service-operations lakehouse and acceptance queries.
Business measures
- Define operational decisions and acceptance queries
- State analytical grain and time windows
Data contracts
- Write source contracts and quality expectations
- Map history and correction requirements
Layering
- Choose raw, validated and curated boundaries
- Define SQL and AI consumption contracts
Ownership
- Assign source, platform and product ownership
- Document data sensitivity and access requirements
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: Use-case and lakehouse design
Define a service-operations lakehouse and acceptance queries.
Expected output. Lakehouse design and acceptance contract
Acceptance. The design maps each business question to governed data and measurable acceptance.
2Module 2 · 8 hours
Incremental ingestion
+
Module 2 · 8 hours
Incremental ingestion
Build repeatable ingestion and test replay scenarios.
Source files and events
- Choose source ingestion patterns and credentials
- Identify network and storage dependencies
Schema evolution
- Handle evolving schemas explicitly
- Reject incompatible changes with actionable evidence
Checkpoints
- Track checkpoints or incremental state
- Plan replay, backfill and recovery
Replay and duplicate handling
- Detect duplicate business records
- Reconcile first and repeated loads
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
Build repeatable ingestion and test replay scenarios.
Expected output. Incremental ingestion evidence
Acceptance. Ingestion handles duplicates and replay without unexplained record loss.
3Module 3 · 8 hours
Transformation and pipeline engineering
+
Module 3 · 8 hours
Transformation and pipeline engineering
Implement curated datasets and quarantine invalid records.
Lakeflow pipelines
- Use current Lakeflow pipeline concepts
- Separate transformations from orchestration
Quality expectations
- Define quality expectations and quarantine handling
- Associate failures with source and rule
History
- Implement agreed history and correction behaviour
- Verify joins and aggregation grain
Performance
- Inspect performance and file-layout considerations
- Avoid tuning without workload evidence
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: Transformation and pipeline engineering
Implement curated datasets and quarantine invalid records.
Expected output. Validated lakehouse transformations
Acceptance. Curated outputs preserve business history and pass reference and quality tests.
4Module 4 · 8 hours
Governance and analytical serving
+
Module 4 · 8 hours
Governance and analytical serving
Expose approved data to two personas and verify denied access.
Unity Catalog privileges
- Grant Unity Catalog privileges to intended groups
- Separate developer and runtime identities
Service identities
- Publish approved SQL tables or views
- Keep underlying storage access consistent with governance
SQL consumption
- Verify consumer queries and denied access
- Document sharing boundaries
Lineage
- Inspect available lineage and ownership
- Track business definitions with analytical outputs
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: Governance and analytical serving
Expose approved data to two personas and verify denied access.
Expected output. Governed analytical data product
Acceptance. Consumers receive only approved data and metrics reconcile through the SQL interface.
5Module 5 · 8 hours
Bounded AI consumption
+
Module 5 · 8 hours
Bounded AI consumption
Build a small read-only question-answer interface over curated data with reference checks.
SQL-based question answering
- Define a read-only question-answer contract
- Choose direct SQL references for expected answers
Approved model access
- Use an approved model client with structured query constraints
- Keep generated SQL within allowed operations
Query validation
- Validate query shape and restrict allowed sources
- Apply resource-side authorisation
Read-only tools
- Compare responses with reference queries
- Reject unsupported or unsafe requests
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: Bounded AI consumption
Build a small read-only question-answer interface over curated data with reference checks.
Expected output. Bounded AI interface and test record
Acceptance. The bounded interface answers reference questions and cannot perform write operations.
6Module 6 · 8 hours
Operational capstone
+
Module 6 · 8 hours
Operational capstone
Run the complete solution and demonstrate reconciliation, security and recovery.
Jobs
- Orchestrate the full pipeline with scoped identity
- Capture run and reconciliation evidence
Monitoring
- Inspect failures and operational dependencies
- Demonstrate a controlled recovery
Cost
- Estimate workload and serving costs
- State capacity and performance limits
Recovery and handover
- Provide support ownership and data contracts
- Present acceptance and unresolved risks
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: Operational capstone
Run the complete solution and demonstrate reconciliation, security and recovery.
Expected output. Assessed Databricks data and AI solution
Acceptance. The capstone passes data, access, replay and bounded AI-consumption checks.
Capstone
Run the complete solution and demonstrate reconciliation, security and recovery. Assessed Databricks data and AI solution Evidence of relevant access, quality and risk controls Demonstration and limitations record
Assessment
Scenario-based practical assessment Deliver governed lakehouse ingestion, serving and bounded AI access 30% Prove quality, history, replay and reference-query correctness 25% Restrict runtime data privileges and prohibit write actions 25% Present operational evidence and accountable product ownership 20% Proposed VNode outline-level assessment; no vendor accreditation or completed lab validation implied.
Participant deliverables
- Assessed Databricks 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 lakehouse data product with a bounded AI consumption interface.
- 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
