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Faster path from data model intent to production-ready DBT build

$199.00
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A tailored course, built for your situation

Faster path from data model intent to production-ready DBT build

Turn high-impact data engineering decisions into working pipelines in hours, not cycles

$199 one-time
24-hour access provisioning 30-day money-back guarantee Hand-built implementation playbook
12 modules. 12 chapters per module. 144 chapters total.
12 modules, each with 12 chapters (144 chapters total), text-based, plus downloadable templates and a hand-built implementation playbook delivered alongside course access.

Who this is for

Data Engineer working in cloud data platforms (Snowflake, Azure) using DBT for transformation and pipeline orchestration

Who this is not for

Engineers focused only on infrastructure setup, pipeline monitoring, or dashboarding without active involvement in data modeling and transformation logic

What you walk away with

  • Deploy DBT models with fewer revision cycles by locking requirements early
  • Use pre-validated structural patterns for facts, dimensions, and incremental loads
  • Align stakeholders faster using executable documentation templates
  • Reduce time from spec to deployed model by applying consistency guards
  • Ship reusable, testable transformation logic that integrates cleanly into broader pipelines

The 12 modules (with all 144 chapters)

Module 1. Map business question to core data entities
Start with the decision the data must support and reverse-engineer the essential entities and relationships needed. Avoid overbuilding by focusing on minimal viable schema.
12 chapters in this module
  1. Identify decision drivers
  2. Extract key nouns and verbs
  3. Define primary grain
  4. List core business events
  5. Map upstream sources
  6. Determine latency needs
  7. Set scope boundaries
  8. Label ownership domains
  9. Document assumptions
  10. Validate with stakeholders
  11. Lock version 0.1
  12. Store in pattern library
Module 2. Structure staging layers for traceability
Design raw and cleansed staging layers that preserve source fidelity while enabling fast diagnosis and audit. Use naming and metadata patterns that make lineage obvious.
12 chapters in this module
  1. Prefix by source system
  2. Preserve original timestamps
  3. Flag record validity
  4. Add load metadata columns
  5. Use consistent casing
  6. Isolate dirty records
  7. Document parsing rules
  8. Version per source change
  9. Index for backfill
  10. Test ingestion idempotency
  11. Log row counts
  12. Link to data dictionary
Module 3. Design durable dimensional models
Build star schemas that survive business changes by separating volatile attributes, managing SCDs properly, and anchoring to stable keys.
12 chapters in this module
  1. Choose natural vs. surrogate keys
  2. Classify attribute volatility
  3. Implement Type 1 changes
  4. Track Type 2 history
  5. Flag current records
  6. Use hashdiffs for changes
  7. Index effective dates
  8. Document grain assumptions
  9. Label conformed dimensions
  10. Test rollback behavior
  11. Validate join correctness
  12. Automate dimension tests
Module 4. Model facts with predictable grain
Define fact tables with unambiguous grain to prevent double-counting and ensure consistent aggregation across use cases.
12 chapters in this module
  1. State the grain clearly
  2. Count one event per row
  3. Use semi-additive measures wisely
  4. Store currency in base unit
  5. Apply timezone normalization
  6. Link to time dimension
  7. Flag incomplete records
  8. Use nullable foreign keys
  9. Test measure stability
  10. Validate null handling
  11. Document business meaning
  12. Version fact logic
Module 5. Write efficient DBT models
Structure.sql files for clarity, reuse, and performance. Use CTEs purposefully and avoid anti-patterns that slow down builds.
12 chapters in this module
  1. Name CTEs descriptively
  2. Isolate business logic
  3. Avoid nested CTEs
  4. Use intermediate layers
  5. Leverage DBT macros
  6. Apply early filtering
  7. Minimize wide selects
  8. Use conditional aggregation
  9. Partition in Snowflake
  10. Cluster by date keys
  11. Test execution time
  12. Document performance tips
Module 6. Apply incremental load patterns
Optimize DBT incremental models to process only new or changed data without missing updates or duplicating records.
12 chapters in this module
  1. Choose merge strategy
  2. Set unique key constraints
  3. Use updated_at filters
  4. Handle late-arriving data
  5. Backfill missing windows
  6. Test edge cases
  7. Monitor watermark drift
  8. Log processed ranges
  9. Detect source gaps
  10. Pause on failure
  11. Validate row counts
  12. Document load logic
Module 7. Build reusable transformation logic
Create shared macros and models that eliminate repetition and ensure consistency across pipelines.
12 chapters in this module
  1. Extract common calculations
  2. Standardize naming
  3. Use macro arguments
  4. Validate input types
  5. Test output correctness
  6. Document usage examples
  7. Version shared logic
  8. Deprecate old versions
  9. Set team adoption rules
  10. Review quarterly
  11. Store in central repo
  12. Enforce linting rules
Module 8. Implement data quality checks
Embed validation directly in DBT to catch issues early, using tests that are specific, actionable, and tied to business impact.
12 chapters in this module
  1. Define null rate thresholds
  2. Set unique key constraints
  3. Validate referential integrity
  4. Check value ranges
  5. Monitor distribution shifts
  6. Test for staleness
  7. Use custom schema tests
  8. Trigger alerts on fail
  9. Log test results
  10. Review historical trends
  11. Adjust thresholds
  12. Document false positives
Module 9. Generate executable documentation
Use DBT docs to create living documentation that reflects current logic and helps onboard teammates and stakeholders faster.
12 chapters in this module
  1. Write clear model descriptions
  2. Annotate column meanings
  3. Link to business glossary
  4. Embed usage examples
  5. Update with each change
  6. Publish automatically
  7. Share with analysts
  8. Include lineage diagrams
  9. Highlight critical models
  10. Tag by domain
  11. Search across projects
  12. Archive outdated versions
Module 10. Orchestrate reliable pipeline runs
Sequence DBT jobs effectively using dependencies, failure handling, and retry logic that keeps pipelines resilient and predictable.
12 chapters in this module
  1. Define job order
  2. Set dependency chains
  3. Use Airflow DAGs
  4. Handle partial failures
  5. Retry on transient errors
  6. Log run metadata
  7. Monitor execution time
  8. Alert on delays
  9. Pause on schema drift
  10. Validate downstream impact
  11. Track run history
  12. Optimize schedule frequency
Module 11. Validate stakeholder alignment
Confirm models meet business needs early with lightweight review protocols that reduce late-stage changes.
12 chapters in this module
  1. Share model interface first
  2. Use sample outputs
  3. Host walkthrough sessions
  4. Collect feedback in writing
  5. Track change requests
  6. Prioritize revisions
  7. Confirm final scope
  8. Document approvals
  9. Link to Jira tickets
  10. Update changelog
  11. Notify downstream users
  12. Archive discussion threads
Module 12. Ship and monitor in production
Transition models from development to production with confidence, using checks, alerts, and ownership protocols that ensure long-term reliability.
12 chapters in this module
  1. Promote via Git flow
  2. Apply production tags
  3. Enable monitoring
  4. Set alert thresholds
  5. Assign owner
  6. Document SLA expectations
  7. Review performance weekly
  8. Track usage metrics
  9. Plan for scaling
  10. Schedule cleanup jobs
  11. Update documentation
  12. Conduct post-mortems

How this maps to your situation

  • Starting a new data domain build
  • Refactoring legacy ETL pipelines
  • Onboarding a new analytics team
  • Scaling existing DBT usage across teams

Before vs. after

Before
Models evolve through multiple iterations, with alignment delays and validation loops slowing deployment.
After
Models move from intent to production in hours, with stakeholder alignment and technical validation built into the process.

What's included with your purchase

  • 12 modules with 12 chapters each (144 chapters)
  • Downloadable templates and worked examples for every module
  • Hand-built implementation playbook delivered alongside course access
  • 30-day money-back guarantee

Delivery and format

  • Course and learning environment access provisioned within 24 hours of purchase
  • Hand-built implementation playbook delivered alongside course access

Format: Text-based modules and chapters in the Art of Service learning environment, plus downloadable templates and worked examples for every chapter, plus the hand-built implementation playbook delivered alongside course access.

Time investment: 2, 3 hours per module, self-paced over 6, 8 weeks

How this compares to the alternatives

Generic DBT tutorials focus on syntax; this course delivers decision-by-decision guidance for shipping faster with fewer revisions. Unlike public workshops, it includes tailored implementation patterns for Snowflake and Azure environments.

Frequently asked

Is this course focused on DBT Cloud or DBT Core?
Covers both, principles apply across DBT environments, with specific configuration examples for self-hosted and managed setups.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help me reduce rework on DBT models?
Yes, by teaching upfront design patterns and validation techniques that prevent common revision cycles.
$199 one-time. 2, 3 hours per module, self-paced over 6, 8 weeks.

Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.

30-day money-back guarantee· 144 chapters· Hand-built playbook included· Account access within 24 hours
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