A tailored course, built for your situation
Mastering AWS Well-Architected for Big Data Practitioners
A step-by-step guide to designing scalable, secure, and high-impact data architectures aligned with enterprise cloud standards
$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.
Feeling like your data architecture insights are heard only within your team?
The situation this course is for
Even senior data engineers and analysts often stay siloed, their recommendations limited to immediate pipelines, not platform-wide decisions. Without a shared, credible framework, influence stops at the team boundary.
Who this is for
Senior Big Data practitioners in consulting or services firms who advise on cloud data architecture but lack formal leverage in platform governance
Who this is not for
Junior developers, non-technical stakeholders, or teams focused solely on on-prem or non-AWS ecosystems
What you walk away with
- Lead cross-functional design reviews using AWS Well-Architected language
- Shape platform decisions before they’re locked in by infrastructure teams
- Build credibility as a go-to advisor across cloud, data, and security units
- Produce artefacts that stand up in architecture board discussions
- Drive consistency in data solutions across regions and client teams
The 12 modules (with all 144 chapters)
Module 1. Foundations of the AWS Well-Architected Framework
Establish core understanding of the five pillars: operational excellence, security, reliability, performance efficiency, and cost optimization within enterprise cloud data environments.
12 chapters in this module
- Defining architectural maturity beyond data pipeline efficiency
- How AWS Well-Architected differs from ad hoc cloud governance
- The role of structured reviews in preventing rework
- Key stakeholders in a multi-team cloud architecture review
- Mapping current data projects to the five pillars
- Identifying gaps in documentation or decision traceability
- Common anti-patterns in Big Data architecture evaluations
- How review frequency affects platform stability
- Documenting assumptions and constraints transparently
- Benchmarking against peer-reviewed cloud architectures
- Integrating feedback loops across development lifecycle
- Versioning and maintaining architecture decision records
Module 2. Operational Excellence in Data-Intensive Systems
Apply best practices for running and monitoring data workflows at scale with emphasis on proactive incident prevention and change validation.
12 chapters in this module
- Designing for automated recovery in streaming pipelines
- Creating actionable runbooks for common data failures
- Implementing change calendars across distributed teams
- Using observability to reduce mean time to detect
- Validating CI/CD pipelines for data model changes
- Documenting operational procedures for handover
- Automating rollback strategies for corrupted datasets
- Establishing service ownership in shared environments
- Measuring operational load on data engineering teams
- Integrating post-mortems into continuous improvement
- Scheduling proactive optimization windows
- Using metrics to justify operational headcount
Module 3. Security Architecture for Cloud Data Platforms
Strengthen data protection strategies across identity, network, and data layers using AWS best practices and zero-trust principles.
12 chapters in this module
- Implementing least-privilege access for ETL jobs
- Securing cross-account data sharing securely
- Encrypting data at rest and in transit by design
- Auditing access patterns for anomaly detection
- Managing secrets and credentials in cloud environments
- Enforcing data classification in pipeline metadata
- Applying network segmentation to data lakes
- Validating compliance with data residency rules
- Integrating with central identity providers
- Building automated policy checks in CI pipelines
- Responding to unauthorized access attempts
- Designing for breach containment and isolation
Module 4. Reliability Engineering for Data Workflows
Design fault-tolerant data systems that maintain integrity and availability under real-world conditions and failure scenarios.
12 chapters in this module
- Defining acceptable data freshness and latency SLAs
- Architecting for region-level resilience in pipelines
- Testing recovery from corrupted intermediate data
- Implementing health checks across transformation layers
- Using redundancy without unnecessary duplication
- Planning for graceful degradation during outages
- Automating retries with exponential backoff
- Monitoring end-to-end data lineage for breakages
- Validating replayability of event streams
- Designing idempotent processing stages
- Documenting failure modes and recovery paths
- Simulating network partition scenarios
Module 5. Performance Efficiency in Data Architectures
Optimize compute, storage, and network usage to ensure data systems scale efficiently and deliver results predictably.
12 chapters in this module
- Selecting appropriate instance types for data jobs
- Tuning parallel processing for throughput
- Optimizing file formats for query performance
- Balancing caching layers with freshness needs
- Reducing data shuffling in distributed processing
- Indexing strategies for high-cardinality datasets
- Pre-aggregation patterns for reporting workloads
- Right-sizing clusters based on demand curves
- Using spot instances for non-critical workloads
- Measuring efficiency per dollar spent
- Benchmarking query performance across versions
- Avoiding performance regressions in deployments
Module 6. Cost Optimization for Enterprise Data Systems
Implement strategies to monitor, analyze, and control cloud spending across data platforms without sacrificing performance or reliability.
12 chapters in this module
- Tagging resources for accurate cost allocation
- Using AWS Cost Explorer for data pipeline visibility
- Right-sizing clusters based on utilization patterns
- Scheduling shutdowns for non-production environments
- Choosing storage tiers based on access frequency
- Forecasting costs for upcoming data initiatives
- Avoiding over-provisioning in auto-scaling groups
- Leveraging reserved instances for stable workloads
- Detecting cost anomalies before they escalate
- Reporting cost efficiency to leadership teams
- Designing for elasticity without overspending
- Building budget alerts with automated remediation
Module 7. Cross-Team Architecture Reviews
Lead effective, collaborative reviews that align data, cloud, and application teams around shared architectural standards.
12 chapters in this module
- Structuring review agendas that drive decisions
- Preparing documentation that scales across teams
- Facilitating consensus on trade-offs between pillars
- Documenting decisions for future reference
- Incorporating security findings into design updates
- Balancing speed with architectural rigor
- Engaging stakeholders from multiple domains
- Using scoring systems to prioritize improvements
- Tracking remediation items post-review
- Creating executive summaries from technical details
- Integrating feedback from external auditors
- Re-running reviews after major changes
Module 8. Data Governance Integration with Architecture
Embed governance practices into architectural design to ensure compliance, discoverability, and trust in data assets.
12 chapters in this module
- Designing metadata capture into pipeline workflows
- Integrating data quality checks at ingestion
- Using tags to enforce classification and access rules
- Automating lineage capture across transformations
- Validating schema changes against business rules
- Enabling self-service discovery through cataloging
- Applying retention policies at the object level
- Auditing access to sensitive datasets
- Enforcing data usage agreements in pipelines
- Linking governance rules to architecture decisions
- Monitoring for policy violations in real time
- Reporting compliance posture to oversight teams
Module 9. Scaling Design Patterns Across Regions
Standardize and replicate successful data architectures across geographies and business units while accommodating local requirements.
12 chapters in this module
- Defining core architecture templates for reuse
- Adapting patterns for local data residency laws
- Managing configuration drift across deployments
- Using infrastructure as code for consistency
- Establishing centralized review for regional variants
- Training regional teams on standard patterns
- Documenting deviations and justifications
- Automating compliance validation globally
- Sharing lessons learned across locations
- Optimizing network costs for inter-region data flow
- Synchronizing updates across replicated systems
- Designing for local failure isolation
Module 10. Stakeholder Communication and Influence
Articulate architectural trade-offs clearly to technical and non-technical audiences to build alignment and secure buy-in.
12 chapters in this module
- Translating technical risks into business impact
- Creating visual narratives for leadership reviews
- Preempting objections with evidence-based reasoning
- Using cost-benefit analysis in design debates
- Aligning architecture choices with business goals
- Presenting options without technical bias
- Simplifying complex trade-offs for executives
- Building credibility through consistent delivery
- Documenting rationale for future reference
- Responding to scrutiny during audits or reviews
- Incorporating feedback without diluting vision
- Earning trust across cloud, data, and security teams
Module 11. Building Reusable Implementation Artefacts
Develop templates, checklists, and playbooks that accelerate future projects and institutionalize best practices.
12 chapters in this module
- Creating standardized review scorecards
- Developing architecture decision record templates
- Building checklists for pre-review preparation
- Designing onboarding materials for new teams
- Producing reusable infrastructure code modules
- Documenting common patterns and anti-patterns
- Assembling evidence packages for auditors
- Creating executive briefing decks from reviews
- Versioning artefacts for continuous improvement
- Integrating artefacts into CI/CD pipelines
- Sharing libraries across client engagements
- Updating templates based on new AWS guidance
Module 12. Sustaining Architectural Leadership
Establish long-term influence by mentoring others, contributing to governance, and evolving practices as technology and business needs change.
12 chapters in this module
- Mentoring junior practitioners in best practices
- Contributing to internal architecture guilds
- Proposing updates to enterprise standards
- Tracking emerging AWS features for adoption
- Measuring the impact of architectural improvements
- Gathering feedback from peer teams
- Publishing internal case studies from reviews
- Leading brown-bag sessions on key topics
- Integrating new regulations into design patterns
- Advocating for architectural investment
- Evolving artefacts with team input
- Measuring influence beyond direct projects
How this maps to your situation
- Post-consulting project transition
- Multi-client architecture advisory
- Cross-regional data platform scaling
- Cloud governance committee participation
Before vs. after
Before
Advised on data pipelines within isolated projects, with limited reach beyond immediate team scope.
After
Leads architecture discussions across data, cloud, and governance teams, shaping platform direction and influencing cross-functional decisions.
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: 90 minutes per week for 12 weeks, with flexible pacing and lifetime access.
If nothing changes
Continuing without structured architectural influence means remaining siloed in execution, missing opportunities to lead platform decisions, and being bypassed when enterprise data strategies are defined.
How this compares to the alternatives
Generic cloud courses teach isolated concepts; this course is tailored to Big Data practitioners needing to lead cross-functional architecture decisions. Unlike certification prep, it delivers actionable artefacts and real-world influence frameworks used by senior teams at AWS enterprise accounts.
Frequently asked
Is AWS experience required?
Familiarity with AWS cloud services is helpful but not required. The course explains core concepts with Big Data context.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
What kind of teams is this for?
Designed for senior data practitioners in consulting and services firms advising on cloud data architecture.
$199 one-time. 90 minutes per week for 12 weeks, with flexible pacing and lifetime access..
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