“The Microsoft Fabric implementation program gave our data engineering team a structured path from legacy pipelines to a modern lakehouse architecture.”
Enterprise Program Brief
AI-Ready Data and Knowledge Engineering with Databricks
Create governed data and retrieval foundations for an enterprise AI application. 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 CodeD04
- MCT-led delivery
- Private cohort / virtual ILT
- Typical response: 1 business day
Scoping call typically scheduled within one business day
VNode ITeS delivers AI-Ready Data and Knowledge Engineering with Databricks as an MCT-led VNode ITeS program for Data engineers, AI data practitioners. 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, AI data practitioners
- Tailored Team Delivery
Overview
Executive overview
Create governed data and retrieval foundations for an enterprise AI application.
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 Databricks knowledge preparation and retrieval
- Measure relevance and verify update and deletion behaviour
- Prove runtime access boundaries before context generation
- Provide reproducible refresh and knowledge-consumption contracts
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.
- Knowledge product contracts
- Source ingestion and provenance
- Preparation and enrichment
- Retrieval and governance
- Quality and lifecycle verification
- Knowledge engineering capstone
1Module 1 · 8 hours
Knowledge product contracts
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Module 1 · 8 hours
Knowledge product contracts
Define required evidence for a policy assistant and its data dependencies.
AI tasks
- Define AI tasks and expected evidence
- Separate answer requirements from model preferences
Sources
- Inventory authoritative documents and tables
- Assign source ownership and purpose
Quality targets
- Set measurable quality and relevance targets
- Include unknown and unsupported cases
Access and freshness
- Define access and freshness obligations
- Identify update and withdrawal dependencies
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: Knowledge product contracts
Define required evidence for a policy assistant and its data dependencies.
Expected output. Knowledge product contract
Acceptance. The knowledge contract names consumers, evidence and measurable acceptance targets.
2Module 2 · 8 hours
Source ingestion and provenance
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Module 2 · 8 hours
Source ingestion and provenance
Build a governed ingestion pipeline with source and access metadata.
Documents and tables
- Ingest sources into governed locations
- Preserve original content and source identifiers
Stable identifiers
- Choose version and effective-date conventions
- Track superseded and corrected content
Versioning
- Capture business ownership and data sensitivity
- Maintain permitted-consumer metadata
Ownership
- Record processing and lineage evidence
- Handle source and parsing failures explicitly
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: Source ingestion and provenance
Build a governed ingestion pipeline with source and access metadata.
Expected output. Traceable knowledge ingestion
Acceptance. Every prepared record has attributable provenance and an access responsibility.
3Module 3 · 8 hours
Preparation and enrichment
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Module 3 · 8 hours
Preparation and enrichment
Prepare context units and test malformed, duplicate and obsolete content.
Parsing
- Parse selected document formats
- Identify extraction failures and incomplete sections
Chunking
- Chunk around coherent information units
- Retain section and citation metadata
Deduplication
- Remove or version duplicate content deliberately
- Prevent stale copies being silently preferred
Quality checks
- Validate completeness and required metadata
- Quarantine unusable content
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: Preparation and enrichment
Prepare context units and test malformed, duplicate and obsolete content.
Expected output. Validated context dataset
Acceptance. The prepared dataset passes explicit metadata and completeness checks.
4Module 4 · 8 hours
Retrieval and governance
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Module 4 · 8 hours
Retrieval and governance
Build and test retrieval with permission-aware access patterns.
AI Search indexes
- Choose index and embedding requirements
- Define refresh and source-table dependencies
Embeddings
- Compare metadata filters and retrieval scope
- Test relevant and irrelevant context
Filtering
- Map Unity Catalog privileges to actual consumer access
- Keep runtime identity distinct from development access
Unity Catalog boundaries
- Document permissions outside the catalog boundary
- Test restricted evidence before generation
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: Retrieval and governance
Build and test retrieval with permission-aware access patterns.
Expected output. Governed retrieval implementation
Acceptance. The retrieval path returns relevant context and respects the tested access design.
5Module 5 · 8 hours
Quality and lifecycle verification
+
Module 5 · 8 hours
Quality and lifecycle verification
Measure retrieval performance and verify updates and withdrawals.
Relevance sets
- Build representative relevance examples
- Use reference passages and expected source identifiers
Coverage
- Measure missing and incorrect evidence
- Compare candidate retrieval configurations fairly
Freshness
- Test freshness after a source correction
- Observe expected propagation delays
Deletion propagation
- Withdraw content and verify downstream removal
- Record gaps requiring reindex or manual remediation
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: Quality and lifecycle verification
Measure retrieval performance and verify updates and withdrawals.
Expected output. Retrieval and lifecycle evaluation report
Acceptance. The evaluation measures relevance and proves update/withdrawal behaviour within scope.
6Module 6 · 8 hours
Knowledge engineering capstone
+
Module 6 · 8 hours
Knowledge engineering capstone
Deliver a reproducible knowledge pipeline and demonstrate access and lifecycle tests.
Refresh automation
- Automate a bounded refresh workflow
- Define dependencies and failure reporting
Monitoring
- Monitor quality, index and consumer readiness
- Assign operators to failed stages
Ownership
- Publish the knowledge consumption contract
- Document permitted use and support ownership
Consumer contract
- Demonstrate access, relevance and lifecycle checks
- Explain limits and unresolved dependencies
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: Knowledge engineering capstone
Deliver a reproducible knowledge pipeline and demonstrate access and lifecycle tests.
Expected output. Assessed Databricks knowledge product
Acceptance. The capstone delivers reproducible knowledge preparation and lifecycle evidence.
Capstone
Deliver a reproducible knowledge pipeline and demonstrate access and lifecycle tests. Assessed Databricks knowledge product Evidence of relevant access, quality and risk controls Demonstration and limitations record
Assessment
Scenario-based practical assessment Deliver governed Databricks knowledge preparation and retrieval 30% Measure relevance and verify update and deletion behaviour 25% Prove runtime access boundaries before context generation 25% Provide reproducible refresh and knowledge-consumption contracts 20% Proposed VNode outline-level assessment; no vendor accreditation or completed lab validation implied.
Participant deliverables
- Assessed Databricks knowledge product
- 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: Create governed data and retrieval foundations for an enterprise AI application.
- 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
