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GEN0361 Mastering AWS Well-Architected for Data Platform QA Engineers

$199.00
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A tailored course, built for your situation

Mastering AWS Well-Architected for Data Platform QA Engineers

Turn cloud architecture reviews into fast, evidence-backed outcomes

$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.

Who this is for

Senior Data QA Engineer at a cloud-first organization, responsible for validating data workflows against architecture and compliance standards

Who this is not for

Junior data analysts, platform administrators without QA responsibilities, or engineers focused solely on raw pipeline development without control validation

What you walk away with

  • Produce architecture-compliant data validation artefacts in under half the usual time
  • Reduce back-and-forth with platform teams by delivering complete evidence packages upfront
  • Apply AWS Well-Architected pillars directly to ETL and DWH workflows with precision
  • Anticipate review feedback and embed controls early in the QA cycle
  • Build reusable templates that accelerate future audits across teams

The 12 modules (with all 144 chapters)

Module 1. Introduction to AWS Well-Architected Framework
Understand the five pillars, operational excellence, security, reliability, performance efficiency, and cost optimization, as they apply specifically to data platform QA. Learn how the framework is used in real audit cycles to validate infrastructure decisions.
12 chapters in this module
  1. Overview of the AWS Well-Architected Framework
  2. How QA Engineers Interact with Architecture Reviews
  3. Key Differences Between DevOps and QA Validation Roles
  4. Understanding the Reviewer’s Expectations
  5. Mapping Data Workflows to Well-Architected Questions
  6. Case Example: ETL Pipeline Audit in a Regulated Environment
  7. Defining Compliance Evidence in Data Contexts
  8. Timing Cycles for Architecture and QA Alignment
  9. Common Gaps in Data Team Readiness for Reviews
  10. Tools Used in Well-Architected Assessments
  11. Role of Automation in Evidence Collection
  12. How This Course Accelerates Your Contribution
Module 2. Operational Excellence in Data Validation
Focus on how to structure data QA activities that support operational excellence. Learn to document workflows, track changes, and respond to incidents with artefacts that satisfy architectural reviewers.
12 chapters in this module
  1. Defining Operational Excellence for Data Teams
  2. Validating Change Management in ETL Workflows
  3. Documenting Incident Response Procedures for QA
  4. Using Runbooks to Strengthen Architecture Alignment
  5. Testing Recovery Procedures with Minimal Downtime
  6. Embedding Feedback Loops into QA Processes
  7. Measuring Process Maturity in Data Workflows
  8. Integrating Peer Reviews into Release Cycles
  9. Designing Self-Service Validation Checklists
  10. Tracking Operational Metrics in QA Reports
  11. Linking QA Outputs to Pillar-Specific Criteria
  12. Common Anti-Patterns in Workflow Documentation
Module 3. Security Pillar and Data Protection
Apply security best practices to data validation, focusing on encryption, access control, and data lineage. Learn how to produce evidence that satisfies security reviewers without deep platform rework.
12 chapters in this module
  1. Understanding the Security Pillar Scope
  2. Validating Data Encryption at Rest and in Transit
  3. Auditing Access Controls for ETL and DWH Systems
  4. Mapping Roles to Data Access Policies
  5. Demonstrating Data Minimization in QA Outputs
  6. Reviewing Audit Logs for Suspicious Activity
  7. Testing Security Incident Detection Capabilities
  8. Validating Secrets Management in Pipelines
  9. Assessing Third-Party Tool Security Posture
  10. Documenting Data Classification Standards
  11. Generating Evidence for SOX or SOC 2 Alignment
  12. Common Security Gaps in Data QA Artefacts
Module 4. Reliability and Data Resilience
Ensure data workflows meet high availability and fault tolerance standards. Learn to validate backup strategies, replication consistency, and recovery mechanisms for mission-critical pipelines.
12 chapters in this module
  1. Defining Reliability in Data Platform Context
  2. Validating Backup and Restore Procedures
  3. Testing Replication Across Environments
  4. Assessing Pipeline Resilience to Node Failures
  5. Documenting Disaster Recovery Runbooks
  6. Verifying Data Integrity After Failover
  7. Checking Monitoring Coverage for Key Pipelines
  8. Evaluating Retry Logic in ETL Jobs
  9. Validating Data Reconciliation Mechanisms
  10. Measuring Uptime for Batch and Streaming Workflows
  11. Producing Evidence of Recovery Readiness
  12. Common Reliability Shortfalls in DWH Systems
Module 5. Performance Efficiency in Data Workflows
Optimize data QA checks for speed and resource use. Learn to validate that pipelines scale efficiently and use cloud resources wisely under real-world loads.
12 chapters in this module
  1. Understanding Performance Efficiency Criteria
  2. Measuring Query Speed Across Data Volumes
  3. Validating Auto-Scaling Configurations
  4. Assessing Resource Utilization in Pipelines
  5. Testing Pipeline Behavior Under Load
  6. Identifying Bottlenecks in ETL Jobs
  7. Validating Indexing and Partitioning Strategies
  8. Reviewing Caching Mechanisms in Data Layers
  9. Benchmarking Against Baseline Performance
  10. Optimizing Logging for Operational Clarity
  11. Generating Performance Evidence for Reviewers
  12. Avoiding Over-Provisioning in QA Validation
Module 6. Cost Optimization for Data Teams
Learn to validate cost-efficient data workflows and identify wasteful patterns. Produce cost-aware QA reports that align with cloud financial governance.
12 chapters in this module
  1. Overview of Cost Optimization in Cloud Environments
  2. Tracking Compute and Storage Spend by Pipeline
  3. Validating Right-Sizing of Data Infrastructure
  4. Assessing Use of Spot or Preemptible Instances
  5. Reviewing Data Retention and Archiving Policies
  6. Measuring Cost per Data Transformation Step
  7. Identifying Idle Resources in DWH Systems
  8. Validating Use of Serverless Components
  9. Comparing Cost Across Environments
  10. Producing Cost Dashboards for Reviewers
  11. Linking QA Findings to Budget Constraints
  12. Common Cost Pitfalls in Data Validation
Module 7. Automating Evidence Collection
Build scripts and templates that auto-generate compliance evidence. Reduce manual effort and ensure consistency across architecture reviews.
12 chapters in this module
  1. Why Automate Evidence Collection
  2. Designing Repeatable QA Validation Scripts
  3. Using APIs to Pull System Configuration Data
  4. Generating Timestamped Audit Trails
  5. Automating Backup Verification Procedures
  6. Validating Access Controls with Code
  7. Integrating with CI/CD Pipelines
  8. Testing Script Accuracy Across Environments
  9. Storing Evidence in Version-Controlled Repositories
  10. Securing Automated Outputs
  11. Maintaining Scripts as Infrastructure Evolves
  12. Common Failures in Automation Logic
Module 8. Cross-Team Validation Workflows
Coordinate QA efforts across data, platform, and security teams. Learn to produce unified artefacts that satisfy multiple reviewers.
12 chapters in this module
  1. Mapping Stakeholder Needs in Architecture Reviews
  2. Aligning QA Timelines with Platform Schedules
  3. Standardizing Evidence Formats Across Teams
  4. Facilitating Joint Validation Sessions
  5. Resolving Conflicting Requirements
  6. Documenting Escalation Paths for Disputes
  7. Integrating Feedback from Multiple Reviewers
  8. Creating Single Sources of Truth for Evidence
  9. Tracking Accountability Across Functions
  10. Reducing Duplication in Validation Efforts
  11. Building Trust Through Transparent Processes
  12. Common Coordination Breakdowns in Reviews
Module 9. Data Lineage and Traceability
Validate end-to-end data flow accuracy and transparency. Learn to map lineage from source to report and satisfy traceability requirements in audits.
12 chapters in this module
  1. Defining Data Lineage for Compliance
  2. Validating Metadata Capture in Pipelines
  3. Mapping Inputs to Business Reports
  4. Testing Lineage Accuracy After Schema Changes
  5. Documenting Data Transformations Step by Step
  6. Using Lineage Tools in Snowflake and AWS
  7. Ensuring Provenance for Regulated Metrics
  8. Reviewing Lineage Gaps in Legacy Systems
  9. Generating Visual Lineage Diagrams
  10. Validating Lineage in Real-Time Workflows
  11. Meeting GDPR and CCPA Traceability Needs
  12. Common Lineage Deficiencies in QA Outputs
Module 10. Quality Assurance in Cloud Migration
Apply data QA practices to cloud migration projects. Ensure moved workflows meet architecture and compliance standards from day one.
12 chapters in this module
  1. Challenges of QA in Cloud Migration
  2. Validating Data Consistency After Migration
  3. Testing Performance in New Environments
  4. Assessing Security Configuration Post-Move
  5. Reviewing Access Controls in Target Systems
  6. Verifying Data Type and Schema Compatibility
  7. Checking Replication Latency in Streaming Jobs
  8. Validating Disaster Recovery in Cloud
  9. Documenting Migration Decisions for Reviewers
  10. Producing Migration Readiness Reports
  11. Avoiding Regression in QA Coverage
  12. Common Pitfalls in Cross-Cloud Transfers
Module 11. Continuous Compliance Monitoring
Shift from periodic to continuous validation. Learn to embed QA checks into ongoing operations so compliance stays current.
12 chapters in this module
  1. From Audit to Continuous Compliance
  2. Designing Real-Time QA Alerts
  3. Automating Pillar-Specific Control Checks
  4. Integrating with Observability Platforms
  5. Reviewing Logs for Policy Deviations
  6. Triggering Revalidations After Changes
  7. Updating Evidence After Infrastructure Drift
  8. Building Dashboards for Compliance Health
  9. Reducing Manual Effort with Automation
  10. Maintaining Up-to-Date Artefacts
  11. Common Gaps in Continuous Monitoring
  12. Scaling Validation Across Multiple Projects
Module 12. Final Review and Implementation Playbook
Synthesize everything into a personalized QA acceleration framework. Receive a hand-built implementation playbook tailored to your current role and responsibilities.
12 chapters in this module
  1. Recap of AWS Well-Architected Pillars
  2. Reviewing Your Personal QA Workflow
  3. Identifying Fastest Wins for Speed Gains
  4. Prioritizing High-Impact Validation Steps
  5. Customizing Templates for Your Team
  6. Testing the Framework on a Live Pipeline
  7. Collecting Peer Feedback on Outputs
  8. Refining the Methodology Over Time
  9. Sharing Results with Stakeholders
  10. Scaling Across Multiple Reviews
  11. Maintaining Relevance as Standards Evolve
  12. Your Next Steps as a QA Accelerator

How this maps to your situation

  • Architecture review cycle acceleration
  • Cross-functional validation ownership
  • First-time pass on internal audits
  • Leadership visibility on QA-led improvements

Before vs. after

Before
Spending weeks preparing for architecture reviews, producing inconsistent artefacts that require multiple rounds of feedback
After
Delivering complete, evidence-backed validation packages in hours, with templates that scale across projects

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 90 minutes per week over six weeks, or one intensive 90-minute session per module on a series of Sundays.

If nothing changes
Without a structured method, QA efforts remain reactive and time-intensive, requiring repeated validation cycles and exposing teams to delays in audit closure.

How this compares to the alternatives

Generic cloud architecture courses teach theory but miss data-specific validation patterns. This course delivers a tailored method for QA engineers to produce working artefacts that pass internal review, no abstraction, no filler.

Frequently asked

Is this course specific to AWS or can it be applied to other cloud providers?
The course uses AWS Well-Architected as a framework, but the validation techniques and evidence structures are cloud-agnostic and apply to GCP, Azure, or hybrid environments.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will I be able to use this immediately in my current role?
Yes. Every module includes templates and examples directly applicable to data QA engineers working on ETL, DWH, and compliance validation tasks.
$199 one-time. Approximately 90 minutes per week over six weeks, or one intensive 90-minute session per module on a series of Sundays..

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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