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Faster path from ETL intent to working pipeline

$199.00
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A tailored course, built for your situation

Faster path from ETL intent to working pipeline

Ship clean, reliable data flows in hours, not days

$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

Data Analyst at enterprise cloud platforms using ADF, DBT, AWS, SQL, and ETL orchestration to deliver structured pipelines

Who this is not for

Entry-level analysts learning SQL, or engineers focused on infrastructure-only pipelines without transformation logic

What you walk away with

  • Deploy working ETL pipelines from concept to production in under 48 hours
  • Use pre-built logic blocks for common transformation patterns in DBT and ADF
  • Reduce dependency on trial-and-error debugging with decision checklists
  • Standardize reusable pipeline templates across projects
  • Ship validated artefacts that integrate seamlessly with AWS and Snowflake stacks

The 12 modules (with all 144 chapters)

Module 1. Pattern-first ETL design
Start with proven pipeline shapes instead of blank canvases. Learn to classify incoming requests into standard patterns and apply matched templates.
12 chapters in this module
  1. Classifying ETL requests by pattern type
  2. Matching specs to template libraries
  3. Identifying core transformation logic early
  4. Avoiding over-customization traps
  5. Setting velocity goals per pattern class
  6. Mapping source systems to standard ingestion types
  7. Using reference architectures as starting points
  8. Defining scope boundaries upfront
  9. Selecting pipeline tools by pattern fit
  10. Documenting assumptions with speed in mind
  11. Building consensus on pattern reuse
  12. Validating pattern fit with stakeholders
Module 2. Template libraries for common flows
Access and customize a curated library of working pipeline templates for star schemas, slowly changing dimensions, and incremental loads.
12 chapters in this module
  1. Sourcing templates from high-velocity teams
  2. Configuring templates for Snowflake DBT use cases
  3. Adapting ADF workflows for common patterns
  4. Parameterizing for source-agnostic execution
  5. Validating template logic before use
  6. Versioning templates for team access
  7. Organizing by data domain and latency need
  8. Securing access to trusted templates
  9. Updating templates after feedback loops
  10. Tagging templates by reliability score
  11. Integrating templates with CI/CD pipelines
  12. Measuring reuse frequency across teams
Module 3. Decision checklists for fast validation
Use targeted checklists to validate pipeline logic early, avoiding late-stage rework and debugging delays.
12 chapters in this module
  1. Building checklists by pattern type
  2. Validating source-to-target mappings
  3. Checking for missing null-handling logic
  4. Confirming timestamp consistency early
  5. Reviewing partitioning strategy fit
  6. Testing incremental logic assumptions
  7. Auditing role-based access design
  8. Spotting anti-patterns in transformation code
  9. Ensuring idempotency by design
  10. Validating error handling paths
  11. Checking documentation completeness
  12. Signing off with checklist completion
Module 4. Pre-built logic blocks for DBT
Integrate modular SQL transformations into new pipelines without rewriting common logic.
12 chapters in this module
  1. Sourcing reusable DBT models
  2. Standardizing date dimension logic
  3. Implementing slowly changing dimension patterns
  4. Handling SCD Type 2 updates efficiently
  5. Validating surrogate key generation
  6. Reusing bridge table logic
  7. Optimizing incremental materialization settings
  8. Testing snapshot logic for accuracy
  9. Documenting model assumptions
  10. Versioning DBT blocks across projects
  11. Integrating with data lineage tools
  12. Sharing blocks across team repositories
Module 5. Pre-built logic blocks for ADF
Deploy standard data movement and orchestration components without rebuilding from scratch.
12 chapters in this module
  1. Sourcing ADF pipeline templates
  2. Configuring trigger-based execution
  3. Setting up parallel copy activities
  4. Integrating key vault for credential handling
  5. Building fault-tolerant retry logic
  6. Designing conditional branching
  7. Validating pipeline dependencies
  8. Parameterizing linked services
  9. Testing pipeline resiliency under load
  10. Monitoring performance thresholds
  11. Versioning ADF components
  12. Deploying via CI/CD gates
Module 6. Fast integration with AWS sources
Connect S3, RDS, and DynamoDB sources to Snowflake using pre-tested ingestion patterns.
12 chapters in this module
  1. Identifying ingestion frequency by source
  2. Setting up S3 event triggers
  3. Parsing nested JSON from Kinesis
  4. Handling schema drift in streaming data
  5. Validating data types on load
  6. Configuring Glue for schema inference
  7. Optimizing batch sizes for cost
  8. Securing cross-account access
  9. Testing end-to-end latency
  10. Validating data consistency checks
  11. Monitoring ingestion pipeline health
  12. Scaling ingestion with auto-healing
Module 7. Snowflake-native transformation design
Leverage Snowflake features to reduce external processing and accelerate pipeline completion.
12 chapters in this module
  1. Using Snowpipe for auto-ingestion
  2. Designing efficient stage tables
  3. Optimizing clustering keys for ETL
  4. Reducing compute with query fusion
  5. Implementing zero-copy cloning for testing
  6. Using tasks for scheduled transformations
  7. Orchestrating with stored procedures
  8. Minimizing data movement costs
  9. Validating transformation logic in stages
  10. Testing performance at scale
  11. Monitoring query efficiency
  12. Refactoring legacy pipelines for speed
Module 8. Validation workflows that don’t slow you down
Embed data quality checks directly into pipelines to catch errors early without delaying delivery.
12 chapters in this module
  1. Choosing validation points by risk level
  2. Implementing row count sanity checks
  3. Validating referential integrity
  4. Checking for unexpected nulls
  5. Using statistical thresholds for anomalies
  6. Integrating Great Expectations into DBT
  7. Setting up automated alerting
  8. Using metadata to track validation history
  9. Documenting exceptions with context
  10. Routing failed records for review
  11. Retrying validation without restart
  12. Reporting pass/fail status to stakeholders
Module 9. Rapid debugging with structured logging
Cut troubleshooting time in half by designing logs that isolate failures quickly.
12 chapters in this module
  1. Standardizing log formats across tools
  2. Tagging logs by pipeline and run ID
  3. Including context in error messages
  4. Capturing input/output for failed runs
  5. Using structured JSON logging
  6. Integrating logs with Snowflake tables
  7. Querying logs for frequent failure modes
  8. Building dashboards for log trends
  9. Setting up alerts on critical errors
  10. Reducing noise in log output
  11. Archiving logs for compliance
  12. Training teammates to read logs
Module 10. Pipeline documentation that keeps pace
Generate clear, up-to-date documentation as a byproduct of development, not a final-step chore.
12 chapters in this module
  1. Extracting docs from DBT models
  2. Using ADF annotations effectively
  3. Generating lineage from code
  4. Automating data dictionary updates
  5. Including assumptions with each module
  6. Linking docs to pipeline runs
  7. Publishing internal documentation sites
  8. Versioning docs with code
  9. Updating docs during refactors
  10. Using docs to onboard new team members
  11. Reviewing docs during sign-off
  12. Measuring documentation completeness
Module 11. CI/CD for reliable, repeatable deployment
Use automated testing and deployment gates to ship confidently without last-minute surprises.
12 chapters in this module
  1. Setting up Git branching for ETL
  2. Building test datasets for validation
  3. Running unit tests on pull requests
  4. Automating schema validation
  5. Deploying with approval gates
  6. Using canary releases for pipelines
  7. Rolling back safely when needed
  8. Testing error recovery paths
  9. Integrating with monitoring tools
  10. Auditing deployment history
  11. Securing production access
  12. Training team on CI/CD workflow
Module 12. From first pipeline to compounding velocity
Turn your fastest wins into reusable assets that accelerate every future project.
12 chapters in this module
  1. Tracking pipeline delivery time
  2. Measuring reuse of templates and blocks
  3. Calculating time saved per project
  4. Sharing success stories with leadership
  5. Proposing expansion of template library
  6. Teaching peers to use standard patterns
  7. Reducing onboarding time for new members
  8. Building team-wide playbooks
  9. Gaining recognition for speed leadership
  10. Shaping future tooling investments
  11. Contributing back to internal knowledge base
  12. Positioning team as high-velocity hub

How this maps to your situation

  • When kicking off a new ETL request
  • When debugging a failing pipeline
  • When onboarding a new data source
  • When scaling existing workflows

Before vs. after

Before
Starting each ETL project from scratch, relying on trial and error, and facing delays from rework.
After
Launching pipelines faster using proven templates, pre-built logic, and checklists that ensure quality from the start.

What's included with your purchase

  • 12 modules with 12 chapters each (144 chapters total)
  • 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 hours per module, designed for integration into real project work.

If nothing changes
Continuing to rebuild similar pipelines manually risks slower delivery, higher error rates, and missed opportunities to lead in speed-driven data cultures.

How this compares to the alternatives

Unlike generic ETL courses, this program delivers specific templates, checklists, and logic blocks tailored to Snowflake, DBT, ADF, and AWS, so you apply what you learn immediately to real pipelines.

Frequently asked

How is this different from general data engineering courses?
It’s focused exclusively on accelerating ETL delivery using reusable artefacts and decision systems already proven in high-velocity environments.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this work with my current tools?
Yes, every module includes direct applications for DBT, ADF, AWS, and Snowflake, with templates you can deploy immediately.
$199 one-time. Approximately 3 hours per module, designed for integration into real project 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
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