Description

This curriculum spans the technical and organizational challenges of integration work seen across multi-workshop architecture planning sessions and internal capability programs, addressing the same decisions teams face when connecting systems in regulated, multi-team environments.

Module 1: Defining Integration Scope and Boundaries

Selecting which systems to include in integration based on data criticality, uptime requirements, and business process dependencies.
Deciding whether to integrate legacy systems via direct database access or through intermediary adapters due to lack of APIs.
Negotiating ownership boundaries between teams when shared data models span multiple departments.
Determining whether integration should support real-time, batch, or event-driven synchronization based on downstream SLAs.
Assessing the impact of third-party vendor API rate limits on integration design and failover strategies.
Documenting integration scope in system context diagrams to align stakeholders on integration touchpoints and exclusions.

Module 2: Evaluating Integration Patterns and Topologies

Choosing between point-to-point and hub-and-spoke patterns based on the number of systems and long-term maintainability.
Implementing message queues for asynchronous communication when downstream systems have variable response times.
Deciding whether to use publish-subscribe for event broadcasting when multiple consumers require the same data.
Introducing an API gateway to manage authentication, rate limiting, and routing across distributed services.
Assessing the trade-offs between centralized ESBs and decentralized microservices-based integration.
Designing request-reply patterns with timeouts and circuit breakers to prevent cascading failures.

Module 3: Data Modeling and Schema Management

Mapping heterogeneous data formats (e.g., XML, JSON, EDI) to a canonical model for consistent transformation.
Resolving naming conflicts in shared fields (e.g., "customer_id" vs. "cust_id") across disparate systems.
Implementing schema versioning to support backward compatibility during system upgrades.
Deciding whether to embed transformation logic in integration middleware or delegate to source/target systems.
Handling null values and missing fields when integrating systems with different data completeness standards.
Validating payloads against schema definitions before transmission to prevent downstream processing errors.

Module 4: Authentication, Authorization, and Security Controls

Configuring mutual TLS between integration endpoints to secure data in transit across untrusted networks.
Implementing OAuth 2.0 client credentials flow for machine-to-machine authentication between backend systems.
Managing secrets for API keys and certificates using a centralized secrets management system.
Auditing access logs to detect unauthorized integration attempts or anomalous data transfers.
Applying role-based access control (RBAC) to restrict which systems can publish or consume specific events.
Masking sensitive data (e.g., PII) in logs and monitoring tools during integration debugging.

Module 5: Error Handling and Resilience Engineering

Designing retry mechanisms with exponential backoff for transient failures in network calls.
Routing failed messages to dead-letter queues for manual inspection and reprocessing.
Implementing idempotency in message processing to prevent duplicate side effects during retries.
Setting up circuit breakers to halt integration flows when downstream systems are unresponsive.
Defining escalation paths for unresolved integration errors that exceed retry thresholds.
Logging correlation IDs across services to trace message flow during incident investigations.

Module 6: Monitoring, Observability, and Performance Tuning

Instrumenting integration pipelines with metrics for message throughput, latency, and error rates.
Configuring alerts for abnormal spikes in message backlog or prolonged processing delays.
Using distributed tracing to identify bottlenecks in multi-hop integration workflows.
Sampling large message payloads for monitoring to balance insight with storage costs.
Profiling transformation logic to optimize CPU-intensive data mapping operations.
Validating SLA compliance by measuring end-to-end latency across integrated systems.

Module 7: Governance, Compliance, and Change Management

Establishing an integration review board to approve new connections and enforce architectural standards.
Documenting data lineage to demonstrate compliance with GDPR, HIPAA, or other regulatory frameworks.
Requiring impact assessments before modifying shared integration contracts or APIs.
Archiving integration logs for a legally mandated retention period to support audit requests.
Coordinating integration deployments with change windows to avoid disrupting business-critical processes.
Deprecating legacy integrations by notifying stakeholders and providing migration timelines.

Module 8: Integration Testing and Deployment Strategies

Creating synthetic test data that mimics production data without exposing sensitive information.
Using contract testing to verify that systems adhere to agreed-upon message schemas.
Staging integration components in a pre-production environment that mirrors production topology.
Implementing blue-green deployment for integration services to minimize downtime during updates.
Simulating network latency and outages to test failover behavior in integration pipelines.
Validating rollback procedures for integration deployments that introduce breaking changes.