A tailored course, built for your situation
Faster path from pipeline spec to working ETL artefact
A 199 course tailored for Databricks data engineers who ship faster by design
$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.
The situation this course is for
Who this is for
Senior data engineer at a cloud-native data platform company, shipping ETL pipelines on Databricks, accountable for timely delivery and production stability
Who this is not for
Entry-level analysts, platform admins without pipeline ownership, or engineers focused on visualisation-only workflows
What you walk away with
- Structure new pipeline specs with built-in validation hooks that prevent downstream rework
- Reduce time from design doc to first working output by applying patterned transformation templates
- Deploy repeatable CI/CD triggers tailored to Databricks workflows
- Anticipate schema drift before it blocks downstream jobs
- Document pipeline rationale in-line so handoffs are frictionless
The 12 modules (with all 144 chapters)
Module 1. Mapping intent to Databricks-ready pipeline stages
Turn high-level requirements into a structured flow across Bronze, Silver, and Gold layers with clear ownership and handoff signals.
12 chapters in this module
- Capturing upstream intent
- Defining layer responsibilities
- Naming conventions that scale
- Versioning at the source
- Tracking change scope
- Setting success criteria
- Validating assumptions early
- Aligning schema expectations
- Routing error signals
- Configuring auto-alert thresholds
- Linking to existing assets
- Documenting design deviations
Module 2. Schema validation before ingestion
Apply lightweight schema enforcement at the entry point to prevent cascading failures and reduce debugging cycles.
12 chapters in this module
- Detecting format drift
- Setting schema tolerance rules
- Auto-rejecting malformed batches
- Logging schema changes
- Triggering notification rules
- Preserving original payloads
- Validating nested structures
- Handling nullability shifts
- Enforcing data types
- Benchmarking validation speed
- Integrating with Unity Catalog
- Tagging risky sources
Module 3. Patterned transformation logic
Use proven transformation patterns that reduce custom code and accelerate testing and review cycles.
12 chapters in this module
- Identifying repeat patterns
- Abstracting common logic
- Building reusable functions
- Parameterising queries
- Enforcing idempotency
- Adding audit columns
- Optimising for partitioning
- Reducing shuffle overhead
- Caching strategic outputs
- Validating output shape
- Documenting transformations
- Versioning transformation rules
Module 4. Automated testing for data quality
Implement lightweight, fast-running tests that catch issues before promotion to higher layers.
12 chapters in this module
- Writing fast smoke tests
- Validating row counts
- Checking completeness rules
- Enforcing uniqueness
- Monitoring distribution shifts
- Testing in staging
- Running pre-merge checks
- Integrating with CI
- Reporting test outcomes
- Alerting on test failure
- Archiving test results
- Updating baselines
Module 5. CI/CD triggers for Databricks workflows
Set up reliable, low-friction deployment pipelines that move changes safely from dev to production.
12 chapters in this module
- Choosing trigger types
- Configuring merge hooks
- Validating branch policies
- Building deployment scripts
- Rolling back safely
- Tagging deployments
- Linking commits to runs
- Auto-documenting changes
- Enabling self-service deploys
- Controlling access levels
- Auditing deployment history
- Monitoring deployment health
Module 6. Monitoring pipeline health in production
Establish clear signals for pipeline performance and data freshness to enable fast response.
12 chapters in this module
- Tracking job duration
- Setting freshness alerts
- Monitoring resource usage
- Identifying backlog growth
- Logging execution metadata
- Visualising pipeline flow
- Alerting on failure chains
- Detecting processing lag
- Correlating with upstream
- Auto-restarting transient jobs
- Capturing error context
- Routing incidents to owners
Module 7. Handling schema drift proactively
Detect and respond to structural changes in source data before they break downstream processes.
12 chapters in this module
- Detecting new fields
- Identifying removed fields
- Tracking type changes
- Assessing impact scope
- Notifying downstream teams
- Updating transformation logic
- Preserving backward compatibility
- Versioning schema definitions
- Validating migration paths
- Testing with mock drift
- Logging drift events
- Setting drift thresholds
Module 8. Optimising for cost and performance
Tune pipeline execution to reduce compute spend and improve throughput.
12 chapters in this module
- Reviewing cluster efficiency
- Choosing right instance types
- Tuning auto-scaling rules
- Optimising file sizes
- Partitioning by access pattern
- Reducing I/O overhead
- Caching intermediate results
- Scheduling off-peak runs
- Monitoring cost per job
- Setting budget alerts
- Identifying idle resources
- Right-sizing pipelines
Module 9. Documenting for maintainability
Embed clarity into pipeline design so future changes are fast and safe.
12 chapters in this module
- Writing inline comments
- Maintaining READMEs
- Linking to data dictionary
- Capturing ownership details
- Recording decision rationale
- Updating docs on change
- Standardising field descriptions
- Including example queries
- Documenting known issues
- Adding troubleshooting tips
- Preserving historical context
- Archiving deprecated pipelines
Module 10. Handoff protocols for cross-team pipelines
Ensure smooth transitions when pipelines cross team boundaries or ownership changes.
12 chapters in this module
- Defining interface contracts
- Setting SLA expectations
- Documenting handoff points
- Creating shared runbooks
- Establishing escalation paths
- Conducting onboarding sessions
- Transferring ownership
- Auditing access rights
- Updating monitoring alerts
- Validating handoff success
- Scheduling check-ins
- Closing handoff loops
Module 11. Building reusable ingestion templates
Create standardised onboarding flows for new data sources to reduce setup time.
12 chapters in this module
- Identifying common ingestion types
- Building source-specific templates
- Configuring auto-discovery
- Setting default schemas
- Applying tagging rules
- Linking to catalog entries
- Running validation checks
- Generating documentation
- Sharing templates org-wide
- Versioning template updates
- Tracking template usage
- Improving based on feedback
Module 12. Accelerating incident resolution
Reduce time to fix by having clear diagnostics, ownership, and recovery paths.
12 chapters in this module
- Identifying root cause fast
- Accessing execution logs
- Replaying failed batches
- Validating fixes in isolation
- Promoting hotfixes quickly
- Communicating outages
- Logging resolution steps
- Updating runbooks
- Conducting post-mortems
- Sharing learnings
- Updating monitoring rules
- Preventing repeat failures
How this maps to your situation
- When starting a new pipeline project
- When inheriting an unstable pipeline
- When onboarding a new data source
- When responding to production incidents
Before vs. after
Before
Pipeline delivery cycles stretch due to rework, unclear handoffs, and late validation.
After
Working artefacts ship faster, with fewer iterations and stronger predictability.
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: Approximately 3-4 hours per module, designed for integration with active work cycles.
How this compares to the alternatives
Unlike generic data engineering courses, this focuses exclusively on shortening the path from spec to working artefact within Databricks environments , with templates and patterns you can apply immediately.
Frequently asked
Who is this course for?
Senior data engineers working in Databricks who own pipeline delivery from spec to production.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this work for non-Databricks pipelines?
The core patterns apply broadly, but examples and templates are optimised for Databricks and Unity Catalog.
$199 one-time. Approximately 3-4 hours per module, designed for integration with active work cycles..
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