data-integration-and-interoperability

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

1. Identify source, target and consumers.
2. Define latency/consistency needs.
3. Choose pattern: batch, API, CDC, event, stream, file or semantic.
4. Define schema/contract.
5. Define transformation and quality checks.
6. Define error handling/reconciliation.
7. Define lineage and observability.
8. 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

• DAMA-DMBOK (DAMA International)
• EDM Council CDMC (Cloud Data Management Capabilities)

Verification

• Required artefacts produced and linked to scope.
• Decisions, assumptions and risks stated explicitly.
• Quality checks or validation performed.
• Files changed reported with traceability preserved.