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Fix the Monday Query Break: Automated Data Pipeline Validation for Analytics Engineers

$199.00
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A tailored course, built for your situation

Fix the Monday Query Break: Automated Data Pipeline Validation for Analytics Engineers

Stop re-running broken pipelines every week. Deploy self-healing validation guards that protect your models and your time.

$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 spreadsheet that breaks every Monday because a source table schema changed or a freshness check was missed

The situation this course is for

Every Monday, data teams face the 'pipeline hangover' , a cascade of broken queries, failed dashboards, and manual triage. For Analytics Engineers, this means hours spent diagnosing failures that could have been caught earlier. The root cause? Validation is reactive, not proactive. Tests run after breakage, not before. Stakeholders lose trust. Engineers lose time. The cycle repeats weekly.

Who this is for

Analytics Engineers in mid-senior roles at data-first companies who own end-to-end pipeline reliability but lack automated validation frameworks to prevent recurring failures

Who this is not for

Data analysts who only consume tables, data scientists focused on modeling, or executives who don't touch code

What you walk away with

  • Deploy a self-validating pipeline framework that runs pre-execution health checks
  • Automate schema drift detection for upstream tables and trigger alerts or fallbacks
  • Reduce weekly pipeline triage time from hours to minutes
  • Implement freshness and row-count guards that prevent stale or empty data propagation
  • Integrate validation rules directly into dbt workflows with zero runtime overhead

The 12 modules (with all 144 chapters)

Module 1. The Anatomy of a Monday Break
Break down real-world pipeline failures: schema drift, late arrivals, null spikes, and silent failures. Map each to a preventable root cause.
12 chapters in this module
  1. When queries break on Monday
  2. The cost of manual triage
  3. Schema drift in Snowflake tables
  4. Late data arrival patterns
  5. Silent failures in dbt runs
  6. Downstream impact chains
  7. Common anti-patterns in alerting
  8. Why testing after run fails
  9. The illusion of pipeline stability
  10. How teams misdiagnose root cause
  11. The role of metadata monitoring
  12. Building a failure taxonomy
Module 2. Validation Before Execution
Shift from reactive to proactive validation. Learn how to run checks before queries execute, not after they fail.
12 chapters in this module
  1. Pre-flight over post-mortem
  2. Guard clauses in SQL workflows
  3. Runtime assertions in dbt
  4. Detecting empty result sets early
  5. Row count sanity thresholds
  6. Timestamp gap detection
  7. Schema compatibility checks
  8. Automated null rate alerts
  9. Dependency freshness guards
  10. Query plan validation
  11. Execution time baselines
  12. Fail fast, fix sooner
Module 3. Automating Schema Drift Detection
Catch schema changes before they break models. Use metadata queries and change logs to trigger adaptive responses.
12 chapters in this module
  1. What is schema drift
  2. Snowflake INFORMATION_SCHEMA paths
  3. Tracking COLUMN changes over time
  4. Automated drift alerts via task
  5. Versioning table interfaces
  6. Fallback schema patterns
  7. Dynamic SQL resilience
  8. Column alias mapping tables
  9. Drift impact scoring
  10. Safe default values
  11. Backfill triggers on change
  12. Documentation sync on drift
Module 4. Freshness Guards and Time Windows
Ensure data arrives on time. Build time-aware checks that validate freshness and prevent stale propagation.
12 chapters in this module
  1. What is data freshness
  2. MAX(timestamp) expectations
  3. Threshold-based alerts
  4. Time zone alignment traps
  5. Partition gap detection
  6. Expected vs actual arrival
  7. SLA tracking for sources
  8. Automated delay notifications
  9. Backfill detection logic
  10. Time-bounded row counts
  11. Freshness in dbt tests
  12. Escalation paths for delays
Module 5. Row Count Stability Patterns
Catch empty or inflated results early. Use statistical baselines to detect anomalies before downstream impact.
12 chapters in this module
  1. Normalizing row counts
  2. Daily variance thresholds
  3. Historical baselines in Snowflake
  4. Spike detection logic
  5. Zero-row prevention
  6. Duplicate detection guards
  7. Partition-level checks
  8. Anomaly scoring methods
  9. Automated rollback triggers
  10. Threshold tuning over time
  11. Drift vs anomaly distinction
  12. Alert fatigue reduction
Module 6. Integrating Validation into dbt
Embed validation logic directly into your dbt project. Use tests, hooks, and custom macros to automate protection.
12 chapters in this module
  1. dbt test fundamentals
  2. Custom schema.yml checks
  3. Macro-based validation
  4. Pre-hook assertions
  5. Post-hook verifications
  6. Test chaining logic
  7. Dynamic test generation
  8. Environment-aware checks
  9. CI/CD integration
  10. Test coverage reporting
  11. Error handling in Jinja
  12. Performance impact tuning
Module 7. Building Self-Healing Pipelines
Design pipelines that adapt to change. Use fallbacks, defaults, and alerts to maintain continuity without intervention.
12 chapters in this module
  1. What is self-healing data
  2. Fallback table strategies
  3. Default value injection
  4. Automatic retry logic
  5. Alert-first escalation
  6. Dynamic source selection
  7. Graceful degradation
  8. Versioned model routing
  9. Health-based routing
  10. Status dashboard integration
  11. Automated documentation updates
  12. Runbook linkage
Module 8. Monitoring Without Noise
Avoid alert fatigue. Build smart monitoring that surfaces only actionable issues.
12 chapters in this module
  1. Signal vs noise in alerts
  2. Deduplication strategies
  3. Alert grouping logic
  4. Escalation tiers
  5. Silence windows
  6. Notification channel rules
  7. PagerDuty integration
  8. Slack alert formatting
  9. Incident tagging
  10. MTTR tracking
  11. False positive reduction
  12. Automated resolution notes
Module 9. Documentation That Stays Current
Keep docs in sync with code. Automate updates so they reflect real state, not assumptions.
12 chapters in this module
  1. dbt docs generate
  2. Auto-updating READMEs
  3. Schema change logging
  4. Column lineage tracking
  5. Owner assignment automation
  6. Description templating
  7. Impact propagation maps
  8. Versioned doc snapshots
  9. Searchable changelogs
  10. Integration with Slack
  11. Access pattern annotations
  12. Usage-based highlighting
Module 10. Testing in Development Environments
Catch issues before merge. Use dev environments to validate changes in isolation.
12 chapters in this module
  1. Branch-level testing
  2. Dev schema strategies
  3. Test data generation
  4. Backfill simulation
  5. Cross-environment checks
  6. Merge conflict prevention
  7. Pull request automation
  8. Preview environment setup
  9. Data size scaling
  10. Performance regression tests
  11. Cost impact estimation
  12. Cleanup automation
Module 11. Scaling Validation Across Teams
Standardize practices across squads. Share reusable validation components and enforce consistency.
12 chapters in this module
  1. Shared macro libraries
  2. Centralized test registry
  3. Team onboarding playbooks
  4. Cross-team SLAs
  5. Validation as code
  6. Policy as code integration
  7. Governance workflows
  8. Approval pipelines
  9. Audit trail generation
  10. Change request automation
  11. Versioned policy enforcement
  12. Feedback loop collection
Module 12. Sustaining Pipeline Health Long-Term
Maintain resilience over time. Use feedback loops and metrics to keep validation effective.
12 chapters in this module
  1. Health score dashboards
  2. Triage time tracking
  3. Failure recurrence rates
  4. Validation coverage metrics
  5. Adoption rate monitoring
  6. Tech debt quantification
  7. Improvement backlog
  8. Quarterly validation audit
  9. Team health reviews
  10. Automation maturity model
  11. Continuous improvement cycle
  12. Retrospective integration

How this maps to your situation

  • When a source table schema changes unexpectedly
  • When data arrives late breaking freshness SLAs
  • When a dbt model runs but produces empty results
  • When stakeholders lose trust due to broken reports

Before vs. after

Before
Spending Monday mornings triaging broken pipelines, manually checking schema changes, and explaining delays to stakeholders.
After
Waking up to healthy pipelines, automated alerts for real issues, and confidence that models self-protect.

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 2 hours per module, designed to be completed incrementally alongside regular work.

If nothing changes
Without automated validation, pipeline breaks will continue to drain engineering time, erode stakeholder trust, and block progress on higher-value work like modeling and optimization.

How this compares to the alternatives

Unlike generic data quality courses, this course focuses on operational validation patterns specifically for Analytics Engineers using Snowflake and dbt. No theory , just deployable code patterns and real-world fixes.

Frequently asked

Who is this course for?
Analytics Engineers who own pipeline reliability and want to stop firefighting recurring breaks.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Do I need to be using dbt?
Yes, the course assumes dbt is part of your workflow and builds validation directly into it.
$199 one-time. Approximately 2 hours per module, designed to be completed incrementally alongside regular 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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