Description

This curriculum spans the technical and organizational challenges of microservices adoption, comparable to a multi-workshop program that integrates domain modeling, deployment automation, and resilience engineering as practiced in large-scale DevOps transformations.

Module 1: Service Decomposition and Domain-Driven Design

Determine bounded context boundaries by analyzing transactional consistency requirements and aligning with business capabilities, avoiding over-decomposition that increases operational overhead.
Resolve shared domain logic conflicts by deciding whether to create a shared library or extract a dedicated service, weighing coupling risks against duplication costs.
Implement anti-corruption layers when integrating legacy systems to insulate new microservices from outdated data models and protocols.
Enforce service autonomy by ensuring each microservice owns its database schema, rejecting cross-service queries that bypass service interfaces.
Manage cross-cutting concerns like logging and monitoring without introducing shared middleware dependencies that undermine deployment independence.
Conduct domain event storming sessions with business stakeholders to identify aggregates and domain events that inform service boundaries.

Module 2: Inter-Service Communication and API Design

Select synchronous HTTP/REST versus asynchronous messaging (e.g., Kafka, RabbitMQ) based on latency requirements, reliability needs, and consumer availability.
Design idempotent APIs to handle message duplication in asynchronous communication, especially for financial or inventory operations.
Version public APIs using URL paths or content negotiation, ensuring backward compatibility while deprecating old versions on a defined timeline.
Implement circuit breakers and bulkheads in service clients to prevent cascading failures during downstream service outages.
Define service-level contracts using OpenAPI or AsyncAPI specifications and enforce them through automated contract testing in CI pipelines.
Manage payload size in inter-service calls by applying pagination, field selection, or gRPC streaming for large data transfers.

Module 3: Data Management and Consistency Strategies

Apply the Saga pattern to maintain data consistency across services, choosing between choreography and orchestration based on complexity and observability needs.
Implement Change Data Capture (CDC) to propagate database changes to event streams without coupling services to transaction logs.
Decide between database-per-service and shared-database models based on team autonomy requirements and data consistency constraints.
Handle eventual consistency by designing user interfaces that reflect asynchronous state transitions with clear status indicators.
Use distributed locking mechanisms sparingly, favoring idempotency and optimistic concurrency control to avoid performance bottlenecks.
Enforce data privacy and residency requirements by tagging data at ingestion and routing it to region-specific services or databases.

Module 4: Deployment and Release Automation

Configure blue-green or canary deployments using service mesh or ingress controllers, routing traffic based on health checks and metrics.
Orchestrate database schema migrations alongside service deployments using versioned migration scripts and automated rollback procedures.
Manage configuration per environment using externalized configuration stores (e.g., Consul, Spring Cloud Config) with encryption for secrets.
Implement health checks that reflect actual service readiness, including dependencies on databases and message brokers.
Enforce immutable artifact promotion across environments to prevent configuration drift and ensure reproducibility.
Automate rollback triggers based on error rates, latency spikes, or failed integration tests during staged rollouts.

Module 5: Observability and Distributed Tracing

Correlate logs across services using a shared trace ID propagated in HTTP headers and message metadata.
Sample distributed traces in high-throughput systems to balance observability costs and performance overhead.
Define service-specific SLOs and error budgets using metrics from Prometheus or similar systems to drive incident response.
Aggregate structured logs using a centralized platform (e.g., ELK, Loki) with retention policies aligned with compliance requirements.
Instrument custom metrics to track business-critical operations, such as order processing latency or payment failure rates.
Configure alerting rules to minimize noise, ensuring alerts are actionable and routed to on-call engineers via escalation policies.

Module 6: Security and Identity Management

Enforce mutual TLS (mTLS) between services using a service mesh to prevent unauthorized inter-service communication.
Validate JWT tokens at the edge or service mesh layer, ensuring claims are checked for scope, issuer, and expiration.
Implement role-based access control (RBAC) at the service level, synchronizing identity data from a central identity provider.
Rotate secrets automatically using tools like HashiCorp Vault, ensuring short-lived credentials for services and databases.
Audit access to sensitive endpoints by logging authentication and authorization decisions in immutable storage.
Secure service-to-service communication in multi-cloud environments by standardizing on a common identity federation model.

Module 7: Governance and Team Organization

Define API governance policies for versioning, deprecation, and performance standards, enforced through automated API gateways.
Establish cross-team coordination mechanisms for shared infrastructure, such as service mesh or monitoring platforms.
Balance standardization and autonomy by curating a platform team-provided stack while allowing opt-outs with documented justification.
Track technical debt in service repositories using issue tagging and periodic architecture reviews to prevent degradation.
Measure team lead time, deployment frequency, and change failure rate to assess DevOps maturity and identify bottlenecks.
Conduct blameless postmortems for production incidents, focusing on systemic improvements rather than individual accountability.

Module 8: Resilience and Disaster Recovery

Test failure modes using chaos engineering tools (e.g., Chaos Monkey) to validate circuit breakers, retries, and fallback logic.
Design multi-region deployments with active-passive or active-active strategies based on RTO and RPO requirements.
Replicate critical stateful data across regions using asynchronous replication with conflict resolution strategies.
Simulate network partitions to verify service behavior under degraded connectivity and split-brain scenarios.
Document and regularly test rollback and data recovery procedures for critical services and databases.
Isolate tenant data in multi-tenant services to ensure failure or breach in one tenant does not impact others.