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Stop Rewriting Databricks ETL Pipelines Every Week

$199.00
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A tailored course, built for your situation

Stop Rewriting Databricks ETL Pipelines Every Week

A field-tested system to build once, run forever data pipelines in Databricks

$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.
Spending every Monday fixing broken ETL pipelines in Databricks?

The situation this course is for

Data engineers in high-velocity environments spend 15, 20 hours weekly reworking pipelines that break due to unhandled schema changes, source API shifts, or configuration drift. The tools exist in Databricks to prevent this, but without a structured implementation pattern, teams keep rebuilding the same logic. This course delivers the missing operational playbook: how to design idempotent, self-documenting, failure-resilient pipelines using Delta Lake, Unity Catalog, and workflow orchestration patterns that reduce rework by 80%.

Who this is for

IC-level Data Engineer working in Databricks, responsible for maintaining and scaling ETL workflows under pressure to deliver reliable data with minimal downtime.

Who this is not for

This is not for data scientists, analysts, or architects who don’t write or maintain production ETL code. It’s not for those who only use Databricks for querying or dashboarding.

What you walk away with

  • Deploy ETL pipelines that survive source schema changes without manual fixes
  • Automate error handling and data quality validation in Databricks workflows
  • Reduce pipeline debugging time from hours to minutes
  • Implement idempotent jobs that prevent duplicate or lost data
  • Document pipeline behavior automatically using Unity Catalog annotations and lineage

The 12 modules (with all 144 chapters)

Module 1. Why ETL Pipelines Break in Databricks
Identify the five root causes of pipeline failure in Databricks environments, from unhandled nulls to job timeout misconfigurations, and map them to preventable design gaps.
12 chapters in this module
  1. Schema drift without guardrails
  2. Uncaught source API changes
  3. Hardcoded paths and secrets
  4. Missing idempotency design
  5. No retry logic patterns
  6. Poor error logging setup
  7. Assumed data types
  8. Missing data quality checks
  9. Over-reliance on manual triggers
  10. Lack of version control
  11. Inconsistent environment parity
  12. No automated testing
Module 2. Designing for Stability from Day One
Apply defensive pipeline design principles to pre-empt failure scenarios before writing code, using constraint-based modeling and pre-flight validation checklists.
12 chapters in this module
  1. Define input contracts first
  2. Use schema evolution guardrails
  3. Plan for null propagation
  4. Isolate transformation logic
  5. Separate ingestion from processing
  6. Build with retry boundaries
  7. Design for partial failure
  8. Use consistent naming standards
  9. Version data interfaces early
  10. Document assumptions visibly
  11. Set failure budget thresholds
  12. Map dependencies visually
Module 3. Delta Lake Patterns for Self-Healing Data
Leverage Delta Lake features like schema enforcement, time travel, and ACID transactions to create data tables that protect themselves from corruption.
12 chapters in this module
  1. Enable schema enforcement
  2. Use mergeSchema safely
  3. Apply Z-Order for performance
  4. Implement soft deletes
  5. Use OPTIMIZE and VACUUM
  6. Leverage time travel for rollback
  7. Track lineage with tags
  8. Partition for query speed
  9. Compact small files automatically
  10. Set retention policies
  11. Monitor table health
  12. Audit changes with history
Module 4. Automated Data Quality Validation
Embed data quality checks directly into pipelines using Great Expectations and Databricks workflows to catch issues before they escalate.
12 chapters in this module
  1. Define expectations per table
  2. Fail jobs on critical violations
  3. Log results to monitoring tool
  4. Use row-level validation
  5. Check for completeness
  6. Validate uniqueness constraints
  7. Monitor distribution shifts
  8. Set thresholds dynamically
  9. Integrate with alerts
  10. Visualize data quality over time
  11. Auto-generate validation rules
  12. Reuse checks across pipelines
Module 5. Idempotent Job Design
Structure Databricks jobs so they can be rerun safely without duplicating or corrupting data, using key-based upserts and transactional boundaries.
12 chapters in this module
  1. Use primary keys consistently
  2. Implement merge instead of append
  3. Track processed files in table
  4. Use job run identifiers
  5. Avoid random UUIDs in logic
  6. Design for reprocessing
  7. Clear staging safely
  8. Lock critical sections
  9. Log start and end points
  10. Handle partial commits
  11. Test rerun scenarios
  12. Document idempotency level
Module 6. Error Handling and Retry Logic
Build robust error recovery into every pipeline stage, including exponential backoff, dead-letter queues, and alerting on unrecoverable failures.
12 chapters in this module
  1. Catch exceptions at source
  2. Log full error context
  3. Use try-catch in PySpark
  4. Implement retry with backoff
  5. Set max retry limits
  6. Route failures to DLQ
  7. Monitor retry frequency
  8. Alert on persistent failures
  9. Fail fast when appropriate
  10. Use circuit breaker pattern
  11. Document failure modes
  12. Test error scenarios
Module 7. Orchestration with Databricks Workflows
Design multi-task jobs with dependency management, failure handling, and parameterization to reduce manual intervention and increase reliability.
12 chapters in this module
  1. Break jobs into tasks
  2. Set task dependencies
  3. Pass parameters between tasks
  4. Handle task failure modes
  5. Use timeout settings
  6. Enable email alerts
  7. Log task outputs
  8. Reuse workflows across projects
  9. Parameterize environments
  10. Test workflow logic
  11. Monitor run history
  12. Optimize task concurrency
Module 8. Secrets and Configuration Management
Securely manage credentials and environment-specific settings using Databricks secrets and config files to prevent exposure and misconfiguration.
12 chapters in this module
  1. Store secrets in Databricks vault
  2. Use secret references in code
  3. Avoid hardcoded values
  4. Separate dev/prod configs
  5. Validate config at runtime
  6. Use environment variables
  7. Rotate secrets regularly
  8. Audit secret access
  9. Limit scope of access
  10. Use least privilege
  11. Document configuration flow
  12. Test config changes safely
Module 9. Automated Testing for ETL Code
Apply software engineering testing practices to ETL workflows, including unit, integration, and regression testing within CI/CD pipelines.
12 chapters in this module
  1. Write unit tests for logic
  2. Mock data sources safely
  3. Test edge cases
  4. Validate transformation output
  5. Check for performance regressions
  6. Run tests in CI/CD
  7. Use test datasets
  8. Measure test coverage
  9. Automate test execution
  10. Fail builds on test failure
  11. Reuse test helpers
  12. Document test strategy
Module 10. Documentation That Stays Updated
Generate and maintain accurate pipeline documentation automatically using code comments, Unity Catalog, and pipeline metadata.
12 chapters in this module
  1. Add comments to all code
  2. Use docstrings in functions
  3. Tag tables with descriptions
  4. Leverage Unity Catalog lineage
  5. Auto-generate READMEs
  6. Link to business context
  7. Document ownership clearly
  8. Update docs on change
  9. Use versioned documentation
  10. Publish data dictionaries
  11. Include example queries
  12. Make docs searchable
Module 11. Monitoring and Alerting Setup
Configure proactive monitoring for pipeline health, including SLA tracking, data freshness, and anomaly detection to reduce firefighting.
12 chapters in this module
  1. Track job start and end
  2. Measure pipeline duration
  3. Set SLA violation alerts
  4. Monitor data freshness
  5. Detect volume anomalies
  6. Log execution metrics
  7. Visualize pipeline health
  8. Use Databricks dashboards
  9. Integrate with external tools
  10. Alert on failure rate
  11. Review logs systematically
  12. Audit access and changes
Module 12. The Zero-Touch Pipeline Playbook
Assemble all patterns into a standardized implementation playbook for deploying pipelines that require no weekly maintenance.
12 chapters in this module
  1. Use the pre-flight checklist
  2. Apply schema guardrails
  3. Embed data quality checks
  4. Ensure idempotency
  5. Add retry logic
  6. Secure secrets properly
  7. Test before deployment
  8. Orchestrate with workflows
  9. Enable monitoring
  10. Auto-document everything
  11. Deploy to staging first
  12. Go live with confidence

How this maps to your situation

  • After inheriting brittle pipelines
  • Before launching a new data product
  • During migration to Unity Catalog
  • When onboarding new data sources

Before vs. after

Before
Spending 15+ hours weekly debugging and rewriting ETL pipelines that break due to small changes, inconsistent logic, or missing error handling.
After
Deploying pipelines that run reliably for weeks without intervention, with automated checks, self-documentation, and clear failure recovery paths.

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: 6, 8 hours to complete core modules, with additional time for implementing templates and playbook steps in your environment.

If nothing changes
Continuing to manually fix pipelines weekly will increase technical debt, reduce trust in data delivery, and limit capacity for higher-value work like analytics engineering or pipeline optimization.

How this compares to the alternatives

Unlike generic Databricks certifications or YouTube tutorials, this course focuses exclusively on operational resilience, giving you field-tested patterns used in production by senior data engineers to eliminate recurring pipeline failures.

Frequently asked

Is this course only for Python users?
No. While examples use PySpark, the design patterns apply to SQL, Scala, and other languages used in Databricks.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this work with our existing Databricks setup?
Yes. The patterns are designed to integrate into existing workflows, regardless of current maturity level.
$199 one-time. 6, 8 hours to complete core modules, with additional time for implementing templates and playbook steps in your environment..

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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