“The Microsoft Fabric implementation program gave our data engineering team a structured path from legacy pipelines to a modern lakehouse architecture.”
Enterprise Program Brief
SQL and Data Engineering Foundations
Create a small analytical dataset with repeatable ingestion and quality checks. 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 CodeF05
- MCT-led delivery
- Private cohort / virtual ILT
- Typical response: 1 business day
Scoping call typically scheduled within one business day
VNode ITeS delivers SQL and Data Engineering Foundations as an MCT-led VNode ITeS program for Data professionals, Technical analysts. 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 professionals, Technical analysts
- Tailored Team Delivery
Overview
Executive overview
Create a small analytical dataset with repeatable ingestion and quality checks.
Environment and licensing
SQL execution environment with the supplied relational sample database Synthetic raw files and a documented load mechanism Read/write permissions limited to the training database Delivery depends on environment and lab validation. This outline does not include step-by-step lab instructions.
Prerequisites
- Basic data literacy
Program Outcomes
Capabilities your teams will gain
- Produce analytical tables at the agreed business grain
- Reconcile joins, totals, increments and rerun results
- Handle malformed and rejected data with explicit ownership
- Deliver source contracts, reference queries and recovery notes
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.
- Querying relational data
- Combining and shaping data
- Data modelling and contracts
- Reliable data movement
- Data product project
1Module 1 · 8 hours
Querying relational data
+
Module 1 · 8 hours
Querying relational data
Query a fictional service-operations dataset and verify totals.
SELECT and filters
- Select and filter records with explicit predicates
- Explain query grain and row counts
NULL handling
- Handle NULLs using deliberate business rules
- Avoid unsafe assumptions about empty strings and missing values
Aggregates
- Aggregate counts, sums and distinct values
- Reconcile totals against a reference dataset
Data types
- Choose appropriate date, numeric and string types
- Inspect conversions that can lose information
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: Querying relational data
Query a fictional service-operations dataset and verify totals.
Expected output. Verified SQL query set
Acceptance. Query results match supplied reference counts and totals.
2Module 2 · 8 hours
Combining and shaping data
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Module 2 · 8 hours
Combining and shaping data
Create analytical transformations and explain grain and join cardinality.
Joins
- Choose join types according to business requirements
- Detect row multiplication from many-to-many joins
CTEs
- Use CTEs to express staged transformations
- Separate readability from execution assumptions
Window functions
- Use window functions for ranking and deduplication
- Define deterministic tie-breaking rules
Deduplication
- Compare latest-record and full-history requirements
- Reconcile records before and after joining
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: Combining and shaping data
Create analytical transformations and explain grain and join cardinality.
Expected output. Transformation queries and reconciliation record
Acceptance. Transformations preserve the intended grain and explain unmatched and duplicate records.
3Module 3 · 8 hours
Data modelling and contracts
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Module 3 · 8 hours
Data modelling and contracts
Design a model and source-to-target contract for operational reporting.
Keys
- Define business and surrogate keys
- Identify uniqueness and referential integrity rules
Fact and dimension concepts
- Choose fact grain and dimension attributes
- Separate operational and analytical model needs
History
- Explain slowly changing history requirements
- Identify late-arriving reference data
Schema and ownership
- Specify schema, ownership and change expectations
- Write source-to-target mapping and quality rules
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: Data modelling and contracts
Design a model and source-to-target contract for operational reporting.
Expected output. Data model and source contract
Acceptance. The model and contract allow another practitioner to implement the agreed output.
4Module 4 · 8 hours
Reliable data movement
+
Module 4 · 8 hours
Reliable data movement
Implement a repeatable load with duplicate, late and invalid records.
Batch and streaming concepts
- Contrast scheduled batch with event/stream processing
- Choose latency according to business need
Incremental loads
- Use watermark-based incremental extraction
- Record progress only after a successful load
Idempotency
- Make repeated execution idempotent
- Identify replay and duplicate risks
Quality and reconciliation
- Quarantine invalid records and reconcile counts
- Separate correctness failures from temporary operational errors
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: Reliable data movement
Implement a repeatable load with duplicate, late and invalid records.
Expected output. Load logic and quality checks
Acceptance. Rerunning a completed load does not change valid totals or silently discard rejected data.
5Module 5 · 8 hours
Data product project
+
Module 5 · 8 hours
Data product project
Deliver an analytical dataset and demonstrate rerun behaviour and data-quality exceptions.
Orchestration concepts
- Define pipeline dependencies and execution order
- Choose measurable completion and failure signals
Monitoring
- Capture row counts and validation results
- Document alert and retry ownership
Access requirements
- Separate producer and consumer permissions
- Review data retention and source provenance
Acceptance tests
- Demonstrate agreed acceptance queries
- Provide rerun and failure-recovery guidance
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: Data product project
Deliver an analytical dataset and demonstrate rerun behaviour and data-quality exceptions.
Expected output. Assessed data engineering project
Acceptance. The final dataset meets reference queries and includes quality and rerun evidence.
Capstone
Deliver an analytical dataset and demonstrate rerun behaviour and data-quality exceptions. Assessed data engineering project Evidence of relevant access, quality and risk controls Demonstration and limitations record
Assessment
Scenario-based practical assessment Produce analytical tables at the agreed business grain 30% Reconcile joins, totals, increments and rerun results 25% Handle malformed and rejected data with explicit ownership 25% Deliver source contracts, reference queries and recovery notes 20% Proposed VNode outline-level assessment; no vendor accreditation or completed lab validation implied.
Participant deliverables
- Assessed data engineering 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 a small analytical dataset with repeatable ingestion and quality checks.
- 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
