A tailored course, built for your situation
Mastering Data Quality in Modern Data Engineering
A structured path to embed data quality at every layer of your AWS data workflows
$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.
Data quality feels reactive , but your role demands proactive control
The situation this course is for
You're certified, skilled, and responsible for data that must be trusted. Yet without a systematic way to enforce quality at scale, you're stuck firefighting bad pipelines, failed validations, and last-minute corrections. Tools help, but they don’t solve the design gaps upstream.
Who this is for
Mid-to-senior data engineers with cloud certifications and hands-on experience, working in regulated or compliance-sensitive environments where data accuracy, traceability, and security are non-negotiable
Who this is not for
Beginners in data roles, tool-only evaluators, or teams looking for vendor-specific training without process integration
What you walk away with
- Build self-correcting data pipelines with embedded quality checks
- Map data quality rules to compliance and governance frameworks
- Reduce rework by designing validation into ingestion and transformation layers
- Automate monitoring and alerting without overloading pipelines
- Deliver trusted datasets with full lineage and audit readiness
The 12 modules (with all 144 chapters)
Module 1. Foundations of Data Quality in Engineering
Establish the core principles of data quality within modern data engineering, focusing on accuracy, completeness, consistency, and timeliness in distributed systems.
12 chapters in this module
- Defining data quality beyond tools
- The engineer's role in quality assurance
- Compliance-driven design thinking
- Data lifecycle quality checkpoints
- AWS services and quality implications
- Common failure patterns in pipelines
- Linking quality to business outcomes
- Metrics that matter for trust
- Error handling vs. prevention
- Designing for auditability
- Balancing speed and reliability
- Case study: Real-world breakdown
Module 2. Designing Quality at Ingestion
Prevent bad data at the source by enforcing schema, format, and integrity rules during ingestion across batch and streaming sources.
12 chapters in this module
- Ingestion anti-patterns
- Schema validation strategies
- File format integrity checks
- API-based data quality gates
- Streaming ingestion safeguards
- Error quarantine design
- Automated rejection workflows
- Metadata tagging at entry
- Source system handshake patterns
- Data provenance capture
- Rate limiting and quality
- Case study: High-volume ingestion
Module 3. Schema Governance and Evolution
Manage schema changes safely while preserving data integrity across evolving pipelines and downstream consumers.
12 chapters in this module
- Schema versioning models
- Backward compatibility rules
- Automated schema drift detection
- Schema registry integration
- Change approval workflows
- Consumer impact assessment
- Rollback readiness
- Documentation as code
- Testing schema migrations
- Alerting on schema violations
- Governance tooling options
- Case study: Schema rollback
Module 4. Data Validation Layer Design
Build modular, reusable validation layers that run independently of transformation logic and scale across datasets.
12 chapters in this module
- Validation layer architecture
- Rule-based vs. ML detection
- Custom rule scripting
- Threshold-based alerts
- Validation as a service
- Cross-dataset consistency checks
- Null and outlier handling
- Data type enforcement
- Range and domain validation
- Referential integrity checks
- Validation performance tradeoffs
- Case study: Cross-system mismatch
Module 5. Automated Monitoring and Alerting
Implement intelligent monitoring that reduces noise and surfaces only meaningful data quality issues.
12 chapters in this module
- Signal vs. noise in alerts
- Threshold tuning strategies
- Dynamic baselining
- Anomaly detection patterns
- Alert routing logic
- Escalation workflows
- False positive reduction
- Dashboarding for engineers
- SLA tracking for data
- Downtime impact analysis
- Incident logging standards
- Case study: Alert fatigue fix
Module 6. Data Lineage and Traceability
Enable full traceability from source to insight, supporting audits, debugging, and compliance reporting.
12 chapters in this module
- Lineage capture methods
- Metadata pipeline design
- End-to-end flow mapping
- Tooling integration options
- Automated lineage extraction
- Versioned lineage tracking
- Impact analysis workflows
- Data ownership tagging
- Audit-ready reporting
- Lineage for debugging
- Privacy-aware lineage
- Case study: Compliance audit
Module 7. Testing Data Pipelines
Apply software engineering test practices to data workflows for reliability and regression prevention.
12 chapters in this module
- Unit testing data transforms
- Integration test design
- Test data generation
- Mocking source systems
- Performance testing
- Regression test automation
- Test coverage metrics
- CI/CD for data pipelines
- Testing in staging environments
- Backfill validation
- Test failure triage
- Case study: Pipeline regression
Module 8. Secure Data Quality Operations
Align data quality processes with security and access controls to prevent exposure during validation and monitoring.
12 chapters in this module
- Secure credential handling
- Role-based access to quality tools
- Data masking in validation
- Audit logging for access
- Encryption in transit and at rest
- Compliance alignment
- SOC 2 and data quality
- Third-party tool security
- Incident response readiness
- Data retention in logs
- Secure alert delivery
- Case study: Security audit pass
Module 9. Building Quality Culture
Shift from individual fixes to team-wide ownership of data quality through standards and collaboration.
12 chapters in this module
- Defining team SLAs
- Quality as a shared metric
- Peer review processes
- Documentation standards
- Onboarding for quality
- Feedback loops with business
- Blameless postmortems
- Quality champion roles
- Incentivizing good practices
- Tooling adoption strategies
- Cross-team alignment
- Case study: Culture shift
Module 10. Data Quality in AWS Ecosystems
Leverage AWS-native services to enforce quality across S3, Glue, Redshift, and Lambda-based pipelines.
12 chapters in this module
- S3 data quality patterns
- Glue DataBrew integration
- Redshift constraint design
- Lambda validation functions
- Step Functions orchestration
- CloudWatch for data alerts
- AWS Config and data rules
- Macie for sensitive data
- QuickSight data monitoring
- Lake Formation quality controls
- AWS DMS validation
- Case study: AWS migration
Module 11. Scaling Quality Across Teams
Standardize data quality practices across multiple teams and pipelines without sacrificing agility.
12 chapters in this module
- Centralized rule management
- Template-driven pipelines
- Quality as code frameworks
- Cross-team governance
- Standardized tooling
- Shared ownership models
- Metrics consistency
- Change control processes
- Training and enablement
- Feedback integration
- Versioned policy enforcement
- Case study: Multi-team rollout
Module 12. Sustaining Data Quality Long-Term
Create feedback loops, continuous improvement cycles, and organizational alignment to keep data quality resilient.
12 chapters in this module
- Postmortem follow-up
- Quality debt tracking
- Improvement backlog
- User feedback integration
- Tooling lifecycle
- Knowledge sharing
- Retirement of legacy checks
- Automation maturity
- Quality maturity model
- Leadership reporting
- Future-proofing design
- Case study: Long-term success
How this maps to your situation
- You're managing AWS data pipelines with increasing quality demands
- You're responsible for compliance and audit readiness
- You're building or scaling a data team with consistent standards
- You're transitioning from reactive fixes to proactive design
Before vs. after
Before
Data quality is reactive, tool-dependent, and inconsistent , leading to rework, compliance risk, and eroding trust in pipelines.
After
Data quality is proactive, standardized, and embedded , enabling trusted, auditable, and scalable data engineering.
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 hours per module, designed for steady implementation alongside your current workload.
If nothing changes
Without a structured approach, data quality issues will continue to surface late, increasing rework, audit risk, and technical debt , ultimately undermining trust in your systems and slowing innovation.
How this compares to the alternatives
Unlike generic data quality courses, this program is tailored to AWS environments and integrates compliance, security, and engineering rigor , with templates and a playbook you can apply immediately.
Frequently asked
Who is this course for?
Data engineers and architects who need to build trusted, compliant, and maintainable data systems in AWS environments.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is there hands-on coding?
No , the course is text-based with templates and examples you can adapt to your environment.
$199 one-time. Approximately 3 hours per module, designed for steady implementation alongside your current workload..
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