Enterprise Data Architecture¶

For CIOs, CDOs, enterprise architects, and platform leaders designing the boundary between analytical and operational systems.

Built to help organizations avoid the most expensive data architecture mistake: turning the enterprise data platform into a general-purpose runtime.

Every enterprise builds a data platform. Most of them also accidentally try to make it run their business operations. This is the guide to not doing that.

This is an architecture position pack -- not a tutorial, not a vendor comparison, not a certification study guide. It draws hard boundaries between enterprise data platforms and operational platforms, provides decision frameworks backed by evidence, and includes reference blueprints with real cloud provider mappings.

Start Here¶

I need to explain platform boundaries to leadership¶

Your stakeholders think the data platform should run everything. Start with the positioning documents:

• What EDP Is -- The problems an enterprise data platform solves and the problems it must not solve
• EDP vs Operational Platform -- Side-by-side comparison across 12 dimensions
• Anti-Patterns -- What breaks when EDP becomes everything

I need a reference blueprint¶

You are designing a target-state architecture or evaluating how platforms coexist:

• Target-State Architecture -- Seven-layer reference architecture with GCP and Azure service mappings
• Capability Architecture -- 35+ capabilities the EDP must provide, with components mapping
• Control Plane -- Metadata, lineage, policy, contracts, audit, and observability infrastructure
• AI/ML Platform Relationship -- How the EDP feeds ML/AI, feature stores, and the feedback loop
• Coexistence Patterns -- How EDP and operational platforms connect through five integration patterns

I need to make an architecture decision¶

• Decision Tree -- "Where does this workload belong?" flowchart with quick reference table
• Capability Map -- 15 business capabilities mapped to platform owners
• Data Mesh -- When data mesh works, when it doesn't, and the pragmatic middle ground
• Maturity Model -- Five levels across six dimensions
• Vendor Evaluation -- 10-dimension evaluation framework with platform archetypes
• Capability Maturity -- 15 capabilities x 4 maturity levels assessment

I need integration patterns¶

• Data Contracts -- Schema, SLAs, ownership, and evolution rules between producers and consumers
• Cost Architecture -- FinOps patterns for data platforms

I need regulatory compliance guidance¶

• Compliance Overview -- Cross-industry regulatory matrix mapped to platform design
• Banking -- BCBS 239 and DORA mapped to architecture decisions
• Healthcare -- HIPAA mapped to data platform design
• Insurance -- Solvency II and IFRS 17 data requirements

I need an operating model¶

• Operating Model -- Roles, processes, measures, and support tiers for running the EDP
• Reliability Model -- Platform SLOs, incident classification, recovery patterns

I need a transformation plan¶

• Transformation Roadmap -- Four stages from current-state confusion to governed coexistence

I need proof this works¶

• Case Studies -- Three anonymized enterprise architecture transformations
• Evidence Tables -- Claims, counter-arguments, failure modes, and measurable outcomes
• Decision Records -- Worked ADR examples applying these frameworks
• Metrics and Outcomes -- Before/after measurements across 13 dimensions
• Review Checklists -- Five operational checklists for architecture review boards

Terminology¶

If the terms in this guide are unclear, start with the Glossary. It defines 14 commonly confused terms and maps each to the target-state architecture.

Related Implementation¶

This repo is the strategy layer. For working implementations:

• reference-data-platform-gcp -- Production-grade EDP on GCP (Data Vault 2.0, dbt, BigQuery, Terraform)
• dbt-data-vault-starter -- Opinionated dbt template for Data Vault 2.0