Catalog
concept#Integration#Architecture#Platform#Software Engineering

System Integration

System integration denotes connecting and aligning heterogeneous applications, data sources and infrastructure into a consistent whole. The goal is interoperable interfaces, reliable data flows and coordinated operational processes.

System integration connects heterogeneous applications, data sources and infrastructure components into consistent, interoperable systems.
Established
Medium

Classification

  • Medium
  • Technical
  • Architectural
  • Intermediate

Technical context

ERP systems (e.g. SAP)CRM platforms (e.g. Salesforce)Message brokers and event platforms (e.g. Kafka)

Principles & goals

Define and version interfaces as contractsPrefer loose coupling and establish clear ownershipPlan observability and error handling from the start
Build
Enterprise, Domain, Team

Use cases & scenarios

Compromises

  • Creation of point-to-point spaghetti connections
  • Data inconsistencies without transactional coordination
  • Insufficient interface security leads to attack surface
  • Version and keep schemas/contracts backward compatible
  • Separate use cases by domain and establish clear ownership
  • Implement observability, tracing and SLOs from the start

I/O & resources

  • Specifications of source systems and APIs
  • Data models and mapping rules
  • Operational requirements and SLAs
  • Defined integration interfaces and adapters
  • Monitoring dashboards and alerting rules
  • Governance documentation and runbooks

Description

System integration connects heterogeneous applications, data sources and infrastructure components into consistent, interoperable systems. It includes interfaces, data mapping, process orchestration and operational concepts to ensure secure and performant information flows. Typical measures are API gateways, ESBs, event brokers and standardized formats; governance and monitoring support implementation and operation.

✔

  • Increased interoperability and reusability of components
  • Improved data quality and consistent business processes
  • Faster onboarding of new services and partners

✖

  • Effort for governance and operations can be significant
  • Legacy systems often require costly adapters
  • Complexity rises with the number of integrated systems

  • Integration latency (P95)

    Time to fully process a message through the integration path; important for SLA assessment.

  • Error rate

    Ratio of failed integration actions to total; indicates stability and data quality.

  • MTTR for integration incidents

    Mean time to recover after integration disruptions; measures operational efficiency.

Retail: POS ↔ Inventory management

Integrating POS systems with central inventory for real-time stock and order processing.

Banking: Core banking and analytics

Synchronizing transaction data into analytics platforms with strict compliance and audit trails.

Manufacturing: MES ↔ ERP

Consistent production data between MES and ERP for planning and traceability.

1

Assess the current interface landscape and requirements.

2

Define integration principles, contracts and governance.

3

Select an integration platform or pattern (API gateway, ESB, event bus).

4

Implement iteratively, start with critical paths, testing and observability.

5

Gradual rollout with monitoring and feedback loops.

⚠️ Technical debt & bottlenecks

  • Undocumented interfaces and proprietary adapters
  • Growing set of ad-hoc transformation scripts
  • No central monitoring, incomplete alerting rules
performance/latencydata-consistencyorganizational-alignment
  • Using integration to paper over missing domain separation instead of organizational change
  • Deploying expensive middleware for isolated needs without a strategy
  • Copying all data without validation or governance
  • Underestimating data model harmonization
  • Ignoring authentication, authorization and auditing
  • Lack of end-to-end tests for integration scenarios
System architecture and integration patternsAPI design, data modeling and mappingOperations, monitoring and incident analysis
Interoperability between heterogeneous systemsScalability and performance of integration pipelinesSecurity, compliance and traceability
  • • Legacy systems without modern APIs
  • • Regulatory requirements and data protection
  • • Limited bandwidth or latency constraints in networks