“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 Engineering Foundations
Create and operate a small governed Databricks data pipeline. Proposed instructor-led duration; delivery subject to environment and lab validation.
VNode ITeSFoundation
Download Program Brief (PDF)Program Snapshot
Engagement scoped to cohort
Duration40 hours
FormatPrivate cohort
LevelFoundation
Course CodeF07
- MCT-led delivery
- Private cohort / virtual ILT
- Typical response: 1 business day
Scoping call typically scheduled within one business day
VNode ITeS delivers Databricks Engineering Foundations as an MCT-led VNode ITeS program for Data engineers, AI engineers. Typical duration is 40 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 engineers
- Tailored Team Delivery
Overview
Executive overview
Create and operate a small governed Databricks data pipeline.
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
- Python and SQL competence
- Data engineering fundamentals
- Python, APIs and Git for AI Engineering, or equivalent demonstrable experience
- SQL and Data Engineering Foundations, or equivalent demonstrable experience
Program Outcomes
Capabilities your teams will gain
- Build raw-to-curated Delta processing and scheduled execution
- Prove incremental, duplicate, invalid-record and rerun behaviour
- Run under scoped catalog and workload permissions
- Provide a reproducible project with lineage and operational handover
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.
- Workspace and compute foundations
- PySpark and Delta tables
- Incremental and declarative pipelines
- Governance and workflow operations
- Lakehouse project and handover
1Module 1 · 8 hours
Workspace and compute foundations
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Module 1 · 8 hours
Workspace and compute foundations
Configure a project and query a governed sample dataset.
Workspace objects
- Navigate notebooks, repos and workspace objects
- Separate shared workspace access from data privileges
Compute choices
- Compare job, interactive, SQL and serverless compute concepts
- Identify cost and policy constraints
Notebooks
- Use parameterised notebooks with documented dependencies
- Separate local development from cloud execution
Unity Catalog hierarchy
- Understand catalog, schema and table ownership
- Query a governed sample without granting broad admin access
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: Workspace and compute foundations
Configure a project and query a governed sample dataset.
Expected output. Workspace and catalog readiness record
Acceptance. The learner can query the intended table and explain compute and catalog boundaries.
2Module 2 · 8 hours
PySpark and Delta tables
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Module 2 · 8 hours
PySpark and Delta tables
Clean source files and write validated Delta tables.
DataFrames
- Create DataFrames with explicit schemas
- Inspect nullability and malformed input
Schemas
- Use column expressions and built-in transformations
- Avoid unnecessary collection to the driver
Transformations
- Read and write Delta tables
- Explain transaction and schema enforcement concepts
Delta reads and writes
- Choose a write mode deliberately
- Reconcile source and destination results
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: PySpark and Delta tables
Clean source files and write validated Delta tables.
Expected output. PySpark transformation notebook
Acceptance. The notebook produces typed Delta outputs and handles invalid rows explicitly.
3Module 3 · 8 hours
Incremental and declarative pipelines
+
Module 3 · 8 hours
Incremental and declarative pipelines
Build a bounded incremental pipeline and test reruns and rejected records.
Incremental ingestion
- Compare incremental batch and streaming requirements
- Identify checkpoint and state responsibilities
Streaming tables
- Define streaming tables and materialized outputs conceptually
- Distinguish current Lakeflow terminology from older DLT examples
Lakeflow terminology
- Apply data quality expectations where supported
- Capture invalid records and failure conditions
Data quality expectations
- Test replay and schema-change assumptions
- Explain when a full refresh is appropriate
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 and declarative pipelines
Build a bounded incremental pipeline and test reruns and rejected records.
Expected output. Incremental pipeline and quality evidence
Acceptance. Incremental processing is repeatable and quality failures are observable.
4Module 4 · 8 hours
Governance and workflow operations
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Module 4 · 8 hours
Governance and workflow operations
Run a job with scoped permissions and diagnose an injected failure.
Catalog privileges
- Grant data privileges to groups and service identities
- Keep interactive and production identities separate
Service principals
- Configure job parameters and dependencies
- Explain retries and task failure propagation
Jobs and parameters
- Inspect logs and job run output
- Diagnose an injected permission or configuration failure
Logs and troubleshooting
- Track ownership and operational access
- Document how credentials and compute are governed
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 workflow operations
Run a job with scoped permissions and diagnose an injected failure.
Expected output. Governed job and access tests
Acceptance. The scheduled job runs with scoped permissions and its injected failure is explained.
5Module 5 · 8 hours
Lakehouse project and handover
+
Module 5 · 8 hours
Lakehouse project and handover
Deliver a raw-to-curated pipeline with lineage, access and rerun evidence.
Source contracts
- Specify accepted source schemas and ownership
- Choose raw and curated table boundaries
Analytical serving
- Publish analytical queries with reference results
- Document consumer permission requirements
Reconciliation
- Demonstrate input/output reconciliation
- Explain rerun and duplicate handling
Operational documentation
- Record dependencies and cleanup
- Hand over a reproducible operational project
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: Lakehouse project and handover
Deliver a raw-to-curated pipeline with lineage, access and rerun evidence.
Expected output. Assessed Databricks lakehouse project
Acceptance. The lakehouse project passes data, access and replay acceptance checks.
Capstone
Deliver a raw-to-curated pipeline with lineage, access and rerun evidence. Assessed Databricks lakehouse project Evidence of relevant access, quality and risk controls Demonstration and limitations record
Assessment
Scenario-based practical assessment Build raw-to-curated Delta processing and scheduled execution 30% Prove incremental, duplicate, invalid-record and rerun behaviour 25% Run under scoped catalog and workload permissions 25% Provide a reproducible project with lineage and operational handover 20% Proposed VNode outline-level assessment; no vendor accreditation or completed lab validation implied.
Participant deliverables
- Assessed Databricks lakehouse project
- 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
- Foundation practitioner depth
- Business outcome alignment
Enterprise Customization
Enterprise customization
Tailor this program to your organization's priorities: Create and operate a small governed Databricks data pipeline.
- 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
