Architecture Lab · Synthetic / Clean-Room Reference

Governed Enterprise Data & AI Reference Implementation

A public implementation path that demonstrates selected engineering patterns from the companion Selected Work case study without exposing client source code, data, exact schemas or proprietary business rules.

Important boundary. The Selected Work case study is based on real professional work. This lab code is written separately from scratch with synthetic data and generic names. It demonstrates the same engineering ideas, but it is not copied or reconstructed client code.

Reference Pattern

Source-aware ingestion

Generic configuration examples for batch, incremental/API, CDC and event ingestion, with common audit metadata and replay expectations.

BatchCDCAPIEvents

Reference Pattern

Snowflake layered model

Small Raw, Curated, Enterprise Identity and Gold examples using synthetic customer records and a stable enterprise identifier.

RawCuratedIdentityGold

Governance Pattern

Governed access

Illustrative role and row-policy patterns showing how internal analytical access differs from customer-safe consumption.

RBACRow PolicySemantic

Agent Pattern

Read-versus-act routing

A minimal Python example routes historical analytical questions to a governed Snowflake tool and current-state/write actions to the owning source-system tool.

MCPAgentTool Routing

Reference repository structure

governed-enterprise-data-ai-reference/
├── README.md
├── config/
│   └── sources.yaml
├── snowflake/
│   ├── 01_raw.sql
│   ├── 02_curated.sql
│   ├── 03_identity.sql
│   ├── 04_gold.sql
│   └── 05_row_access_policy.sql
└── agent/
    └── routing_example.py

Why this is separate from Selected Work

Selected Work explains the real architecture problem, design decisions, trade-offs and production considerations. Architecture Lab is where I can safely demonstrate implementation patterns using clean-room code and synthetic data.

Read the real-work case study →