Description

This curriculum spans the technical and organizational rigor of a multi-workshop architecture engagement, addressing service decomposition, distributed data, automated operations, and team alignment as seen in large-scale internal platform transformations.

Module 1: Strategic Service Decomposition and Domain Modeling

Determine bounded context boundaries using domain-driven design (DDD) event storming sessions with business stakeholders to align service ownership with business capabilities.
Resolve conflicting domain models across teams by establishing context maps and defining anti-corruption layers at integration points.
Decide whether to split or merge services based on coupling metrics, such as shared database tables or frequent synchronous coordination.
Handle cross-cutting concerns like auditing or logging during decomposition by evaluating whether to embed functionality or delegate to infrastructure services.
Assess the impact of transactional consistency requirements when decomposing monolithic modules into separate services with eventual consistency models.
Negotiate service granularity by analyzing deployment frequency, team size, and operational ownership rather than technical convenience.

Module 2: Inter-Service Communication and API Design

Select between synchronous (REST, gRPC) and asynchronous (message queues, event streaming) communication based on latency SLAs and failure tolerance requirements.
Define versioning strategies for public APIs to support backward compatibility while enabling iterative service evolution.
Implement circuit breakers and retry mechanisms with exponential backoff to prevent cascading failures during transient network outages.
Standardize payload schemas using OpenAPI or Protocol Buffers to reduce integration errors and improve client code generation.
Enforce request size limits and rate limiting at the API gateway to prevent denial-of-service conditions from misbehaving clients.
Design idempotent operations for state-changing endpoints to ensure reliability in retry-heavy environments.

Module 3: Data Management and Distributed Transactions

Assign dedicated databases per service and prohibit direct cross-service database access to maintain loose coupling.
Implement the Saga pattern to manage long-running business transactions across services without distributed locking.
Choose between event sourcing and traditional CRUD based on audit requirements, data volatility, and query complexity.
Synchronize read models across services using change data capture (CDC) tools like Debezium in near real time.
Handle referential integrity constraints across services by using eventual consistency and compensating actions instead of foreign keys.
Manage data retention and archival policies independently per service while ensuring compliance with data sovereignty laws.

Module 4: Service Deployment and Lifecycle Automation

Configure independent CI/CD pipelines per service with automated testing, image building, and deployment to staging environments.
Implement blue-green or canary deployments using service mesh or ingress controllers to reduce production rollout risk.
Enforce immutability of deployment artifacts by tagging container images with Git commit hashes and preventing runtime modifications.
Orchestrate rolling updates in Kubernetes with readiness and liveness probes to prevent traffic routing to unhealthy instances.
Manage configuration externalization using tools like HashiCorp Consul or Spring Cloud Config with environment-specific profiles.
Coordinate database schema migrations alongside service deployments using versioned migration scripts in the deployment pipeline.

Module 5: Observability and Runtime Monitoring

Instrument services with structured logging to enable centralized aggregation and correlation across distributed traces.
Deploy distributed tracing using OpenTelemetry to identify latency bottlenecks in cross-service call chains.
Define service-level objectives (SLOs) and error budgets for each critical service to guide reliability improvements.
Configure alerting rules based on golden signals (latency, traffic, errors, saturation) rather than infrastructure metrics alone.
Correlate logs, metrics, and traces using trace IDs propagated through request headers for end-to-end diagnostics.
Limit telemetry data volume and cost by sampling high-cardinality traces in non-production environments.

Module 6: Security and Access Governance

Enforce mutual TLS (mTLS) between services using a service mesh to prevent spoofing and eavesdropping on internal traffic.
Implement OAuth 2.0 with JWT tokens for service-to-service authentication and embed role-based claims for authorization.
Rotate secrets automatically using tools like HashiCorp Vault and prohibit hardcoding credentials in configuration files.
Audit access to sensitive endpoints by logging identity, timestamp, and action for compliance and forensic analysis.
Apply least-privilege principles to service accounts in Kubernetes by defining minimal Role-Based Access Control (RBAC) policies.
Scan container images for known vulnerabilities in the CI pipeline and block deployment of high-risk images.

Module 7: Resilience and Failure Management

Design timeout thresholds for inter-service calls based on upstream service SLOs and network latency baselines.
Implement bulkheads to isolate thread pools or connection limits per dependency and prevent resource exhaustion.
Simulate network partitions and latency spikes using chaos engineering tools like Chaos Monkey in staging environments.
Define fallback responses for non-critical services to maintain partial functionality during downstream outages.
Monitor queue backlogs in asynchronous systems to detect consumer lag and trigger scaling or alerting actions.
Conduct postmortems for production incidents using blameless analysis to update resilience controls and documentation.

Module 8: Organizational Alignment and Operational Maturity

Assign end-to-end ownership of services to dedicated teams using the You Build It, You Run It model.
Establish service catalogs with metadata (owner, SLA, dependencies) to improve discoverability and accountability.
Define escalation paths and on-call rotations for critical services with documented runbooks and incident response procedures.
Standardize technology stacks across teams to reduce cognitive load while allowing exceptions with architectural review board approval.
Measure team lead time, deployment frequency, and change failure rate to assess DevOps maturity and identify bottlenecks.
Conduct architecture review board meetings to evaluate cross-service impacts of major changes and enforce consistency.