A tailored course, built for your situation
Repeatable Data Artefacts That Compound Across Projects
Build self-reinforcing data engineering assets that accelerate every future delivery
$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.
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The situation this course is for
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Who this is for
Mid-to-senior IC data engineer or analyst working across Databricks and Power BI, consistently delivering data pipelines, models, and reporting assets for internal or client use.
Who this is not for
Engineers focused solely on ad-hoc queries or one-off reports without intent to reuse components.
What you walk away with
- A personal IP library of reusable ETL logic templates
- Standardised Databricks notebook structures that reduce onboarding time
- Cross-project data model patterns that maintain compliance and clarity
- A repeatable process for capturing and reapplying data validation rules
- A documented compounding framework to grow influence through asset reuse
The 12 modules (with all 144 chapters)
Module 1. Foundations of Compounding Data Work
Define what makes a data artefact reusable and how compounding differs from reuse. Introduce the concept of engineering yield, how much future effort a current asset saves.
12 chapters in this module
- What compound growth means in engineering
- The cost of non-reuse across projects
- Identifying high-leverage artefacts
- Engineering yield: future savings per unit effort
- The difference between copy-paste and true reuse
- Documenting for retrieval, not just handover
- Versioning logic blocks effectively
- Tagging for discoverability
- Naming conventions that last
- Building personal asset inventories
- When to generalise vs. specialise
- Tracking reuse frequency and impact
Module 2. Designing Reusable ETL Templates
Learn how to structure PySpark and SQL pipelines so they can be redeployed with minimal modification across different data sources and business contexts.
12 chapters in this module
- Parameterising pipeline inputs
- Standardising error handling routines
- Config-driven execution paths
- Isolating business logic from infrastructure
- Reusable data quality checks
- Dynamic schema resolution
- Template annotation for future users
- Testing logic blocks in isolation
- Deployment checklist for templates
- Version control for pipeline components
- Managing dependencies cleanly
- Tracking template performance over time
Module 3. Modular Data Modelling Patterns
Create data models in Databricks that serve multiple reporting and analytics needs, reducing model sprawl and increasing consistency.
12 chapters in this module
- Identifying stable business entities
- Atomic vs. composite grain decisions
- Designing for Power BI star schemas
- Reusable dimension logic
- Standardising conformed dimensions
- Temporal handling patterns
- Fact table extensibility
- Documentation embedded in model code
- Versioning schema changes
- Validating model assumptions
- Cross-domain model reuse
- Indexing for query reuse
Module 4. Building a Personal IP Library
Turn your best work into a searchable, versioned collection of assets you control, regardless of employer platforms or access policies.
12 chapters in this module
- Defining personal ownership of logic
- Secure local archiving strategies
- Metadata tagging for retrieval
- Licensing your own reusable code
- Organising by domain, not project
- Building searchable READMEs
- Versioned release notes
- Open-source style contribution logs
- Keeping libraries framework-agnostic
- Tracking usage across teams
- Updating templates efficiently
- Measuring library adoption
Module 5. Accelerating Power BI Integration
Ensure Databricks outputs align with Power BI consumption patterns, enabling faster dashboard deployment and fewer revisions.
12 chapters in this module
- Optimising for Power BI DirectQuery
- Column naming for semantic layers
- Data type standardisation
- Pre-aggregation strategies
- Embedding business logic in views
- Documentation for analysts
- Reusable measure templates
- Standardising date hierarchies
- Handling slowly changing dimensions
- Validating outputs for reporting
- Version alignment between tools
- Feedback loops from BI teams
Module 6. Embedding Data Governance by Design
Integrate compliance and lineage tracking directly into artefacts so governance keeps pace with reuse.
12 chapters in this module
- Lineage tags in code blocks
- Automated metadata capture
- Standardised PII handling routines
- Compliance annotations
- Audit-ready documentation
- Role-based access patterns
- Data retention flags
- Provenance tracking
- Policy-aware templates
- Governance test suites
- Certification workflows
- Cross-project policy inheritance
Module 7. Capturing and Reapplying Validation Logic
Turn one-off data checks into a growing library of reusable validation rules that improve quality across projects.
12 chapters in this module
- Types of data validation rules
- Reusable null-check routines
- Domain-specific thresholds
- Automated rule injection
- Validation rule versioning
- Documenting false positives
- Sharing rule sets across teams
- Performance impact of validation
- Rule execution logging
- Updating rules without breaking pipelines
- Building confidence metrics
- Feedback from downstream users
Module 8. Creating Self-Documenting Artefacts
Build documentation directly into code and structure so knowledge compounds without additional effort.
12 chapters in this module
- Inline business logic explanations
- Automated doc generation
- Schema change logs
- Consumption examples in README
- Decision rationale capture
- Assumptions tracking
- Dependencies mapping
- Onboarding guides per module
- Standardising doc templates
- Version compatibility notes
- Cross-reference indexing
- Updating docs automatically
Module 9. Scaling Reuse Across Teams
Transition from personal reuse to team-wide adoption by designing for collaboration and clarity.
12 chapters in this module
- Onboarding peers to your templates
- Standardising contribution rules
- Permissioning shared libraries
- Feedback mechanisms
- Version promotion workflows
- Adoption metrics
- Training materials for others
- Integrating with team CI/CD
- Naming conventions for clarity
- Version conflict resolution
- Cross-project coordination
- Maintaining ownership while sharing
Module 10. Measuring the Value of Reuse
Quantify how much time and risk your reusable assets save, both for you and your team.
12 chapters in this module
- Time saved per reuse event
- Reduction in bug rates
- Faster onboarding metrics
- Project acceleration tracking
- Risk reduction from consistency
- Improved audit outcomes
- Peer feedback as metric
- Tracking adoption growth
- Calculating engineering yield
- Reporting reuse impact
- Personal productivity benchmarks
- Comparing reuse vs. rebuild
Module 11. Maintaining Long-Term Asset Value
Keep your reusable components relevant as tools and requirements evolve.
12 chapters in this module
- Scheduling review cycles
- Automated deprecation alerts
- Updating for framework changes
- Backward compatibility strategies
- Breaking changes communication
- User feedback loops
- Usage analytics for maintenance
- Deprecation documentation
- Version archiving
- Migration playbooks
- Retirement criteria
- Succession planning for assets
Module 12. Extending Influence Through Asset Reuse
Position yourself as a multiplier by making your artefacts a default choice across projects and teams.
12 chapters in this module
- Identifying high-impact entry points
- Demonstrating ROI of reuse
- Presenting asset libraries
- Building credibility through consistency
- Influencing tooling choices
- Scaling beyond Databricks
- Contributing to internal marketplaces
- Mentoring through design
- Shaping team standards
- Earning trust via reliability
- Measuring extended influence
- Compounding beyond one role
How this maps to your situation
- Delivering first Databricks pipeline
- Handing off to BI teams
- Onboarding new team members
- Responding to audit requests
Before vs. after
Before
Each new project starts from scratch, with inconsistent structures and undocumented decisions slowing delivery.
After
Every delivery draws on a growing library of trusted components, reducing effort and increasing precision across the board.
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 45 minutes per module, designed to be completed in parallel with ongoing work.
If nothing changes
Without a compounding approach, even excellent work stays isolated, limiting impact, increasing rework, and slowing career momentum.
How this compares to the alternatives
Unlike generic data engineering courses, this program focuses on creating lasting, reusable assets, turning individual expertise into scalable value.
Frequently asked
Is this course about Databricks only?
It uses Databricks and Power BI as primary examples, but the compounding principles apply to any data stack where reuse increases leverage.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will I need to write code?
You'll review and adapt templates, but the focus is on design and reuse patterns, not coding from scratch.
$199 one-time. Approximately 45 minutes per module, designed to be completed in parallel with ongoing work..
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