| name | data-integration-and-interoperability |
| description | Designs batch, API, event, CDC, semantic and file-based integration patterns. Use when connecting systems, choosing integration styles, or resolving interoperability gaps. |
Data Integration and Interoperability
When to use
Use when moving or sharing data between systems, domains, products or platforms.
Objective
Produce a practical, concise, traceable architecture artefact that a coding agent can use to guide implementation or review.
Procedure
- Identify source, target and consumers.
- Define latency/consistency needs.
- Choose pattern: batch, API, CDC, event, stream, file or semantic.
- Define schema/contract.
- Define transformation and quality checks.
- Define error handling/reconciliation.
- Define lineage and observability.
- Define ownership/support.
Required outputs
- Integration pattern decision
- Contract/schema
- Latency/consistency assumptions
- Error/reconciliation approach
- Lineage and support model
Best-practice alignment
Apply DAMA-DMBOK2-style separation of data governance, architecture, modelling, security, integration/interoperability, master/reference data, metadata and quality. For cloud/shared data, apply CDMC-style expectations: ownership, classification, entitlement/access evidence, lineage/provenance, lifecycle/retention, quality controls and auditable evidence.
Quality checks
- Pattern matches latency and coupling needs.
- Schema and semantics are explicit.
- Failures are observable.
- Reconciliation is possible where required.
Avoid
Do not choose streaming where batch is simpler and sufficient.
References
Verification
