metamodel

Enterprise Data Product Metamodel manamagement and the Data Product Life Cycle

Executive Summary

This project defines a comprehensive Enterprise Data Product metamodel strategy and illustrates how it interacts with the Enterprise Data Product Life Cycle. The metamodel provides a structured framework for conceptualizing, designing, implementing, and managing data products within an enterprise context, ensuring alignment with business objectives, governance requirements, and user needs throughout the product lifecycle.

Project Structure

metamodel/
├── .github/
│   └── workflows/
│       └── ci.yml
├── generated/
│   └── .gitkeep
├── metamodel_core/
│   ├── __init__.py
│   ├── data/
│   │   └── metamodel.json      # <-- The core metamodel JSON file
│   ├── models/
│   │   ├── __init__.py
│   │   ├── metamodel.py
│   │   └── validators.py
│   ├── schema/
│   │   ├── __init__.py
│   │   └── protobuf_generator.py
├── scripts/
│   ├── generate_schema.py
│   └── validate_metamodel.py
├── tests/
│   ├── __init__.py
│   ├── conftest.py
│   ├── test_models.py
│   ├── test_schema_generator.py
│   ├── test_validation.py
│   └── test_versioning.py
├── .gitignore
├── AUTHORIZED_INTEGRATORS.md
├── LICENSE
├── pyproject.toml
└── README.md

Here's a vendor-agnostic, technical process for managing the end-to-end lifecycle of an enterprise metamodel, including building, versioning, auditing, approving changes, and enforcing collection of metadata in alignment with the metamodel. This aligns with data engineering best practices and ensures enterprise governance.

---

⚙️ PART 1: Metamodel Lifecycle Management

1. Design and Build

Step	Description
Authoring Format	Use JSON to define the metamodel structure (e.g., entity types, attributes, constraints, validation rules).
Modular Design	Decompose metamodel into reusable modules (e.g., core_entity_types, common_attributes, lineage, governance).
Schema Validation	Define JSON Schema to validate metamodel definitions for structure and allowed values.
CI/CD Integration	Treat metamodel as code in Git. Validate metamodel structure and integrity with pre-commit hooks or GitHub Actions.

2. Versioning

Step	Description
SemVer Versioning	Use Semantic Versioning (MAJOR.MINOR.PATCH) to track metamodel changes.
Immutable Releases	Store versions as immutable artifacts (e.g., Git tag, S3 object with versionId, or package in artifact registry).
Change Logs	Auto-generate changelogs highlighting added/removed/modified fields using tools like json-diff.

3. Change Proposals and Review

Step	Description
Change Proposal Process	Use GitHub Pull Requests with a templated form describing: Motivation Impact to consumers Backward compatibility Validation strategy
Technical Review Board	Approvals by a data architecture committee with representation from governance, engineering, and product.
Validation Suite	Simulate impact on real metadata by running validation pipelines against a test corpus of data products.

4. Approval and Deployment

Step	Description
Approval Artifacts	Signed-off JSON/YAML record of approval metadata (who, when, justification).
Controlled Rollout	Support environment-specific deployment (dev/staging/prod) via IaC or metadata management APIs.
Retirement Policies	Mark old metamodel versions as deprecated with enforcement timelines and migration guides.

---

📏 PART 2: Enforcement and Data Collection Aligned with the Metamodel

1. Registration and Enforcement Framework

Step	Description
Metadata Registry	Implement a registry (e.g., via open-source catalog like Amundsen, DataHub, or internal API) that maps data products to required metadata fields per metamodel version.
Metadata Contracts	Define machine-readable metadata contracts per data product type (dataset, API, model) derived from metamodel.
Enforcement Hooks	Enforce contract adherence at: CI/CD pipeline (dbt tests, ingestion jobs) Data registration time (e.g., Airbyte/Fivetran sync) Publishing time (e.g., Lake Formation registration or catalog sync)

2. Metadata Submission Workflows

Step	Description
Interactive Forms / APIs	Provide validated UIs (e.g., Streamlit) or APIs for metadata input when required fields change.
Slack/Email Notification	Alert affected data product owners when new required attributes are added.
Staging and Validation	Validate metadata submissions in a staging environment. Use JSON Schema validation and business rule engines (e.g., JQ, Great Expectations).

3. Audit and Monitoring

Step	Description
Change Logs & Diffs	Maintain metadata change history for each asset with links to the applied metamodel version.
Dashboards	Track conformance KPIs: percentage of data products meeting latest metamodel, open violations, upcoming deadlines.
Policy Alerts	Raise flags for missing metadata or late adoption of required fields via alerting systems (e.g., Data Observability tool or homegrown script).

---

🧩 Tooling Considerations (Vendor-Agnostic)

Category	Options
Version Control	GitHub / GitLab / Bitbucket
Validation	JSON Schema, Great Expectations, JQ
Packaging	JSON/YAML + CI pipeline (e.g., GitHub Actions)
Registry	PostgreSQL/Neo4j or Open Metadata/Amundsen/DataHub
CI/CD for Metadata	dbt tests, Airflow tasks, GitHub Actions
Notification	Slack, PagerDuty, Email via webhooks
Audit Storage	S3, Lakehouse table with Iceberg/Delta, ElasticSearch

---

✅ Example: New Attribute Enforcement Workflow

Scenario: A new required attribute data_sensitivity_level is added to the metamodel for all datasets.

1. A PR is submitted to add data_sensitivity_level to the core_attributes module.
2. CI runs validation: all test metadata records are scanned.
3. PR is approved by the Data Architecture Committee and tagged as v2.1.0.
4. Deployment pipeline marks v2.1.0 as "default" for dev environment.
5. Slackbot notifies all data product owners with affected assets.
6. UI/API is made available to submit data_sensitivity_level.
7. Metadata registry enforces collection and rejects incomplete registrations.
8. Audit logs track all metadata updates and show coverage over time.

---

PART 3: Visualization of Metamodel Lifecycle Management & Enforcement and Data Collection Aligned with the Metamodel

flowchart TD
  A[Author Metamodel: JSON] --> B[Validate Structure: JSON Schema]
  B --> C[Push to Git: Metamodel as Code]
  C --> D[Create PR with Change Proposal]
  D --> E[Run Validation Tests on Sample Metadata]
  E --> F[Review by Data Architecture Committee]
  F -->|Approved| G[Tag Release: SemVer]
  G --> H[Deploy to Metadata Registry]
  H --> I[Notify Stakeholders: Slack/Email]
  I --> J[Interactive Form/API for Metadata Collection]

  subgraph "Enforcement & Compliance"
    J --> K[Validate Metadata Submissions]
    K --> L[Reject Invalid or Incomplete Assets]
    K --> M[Log Valid Submissions with Version Info]
    M --> N[Audit & Track Conformance Dashboards]
  end

  L --> O[Notify Product Owners of Violations]
  G --> P[Retire Old Metamodel Versions]

  style A fill:#fdf6e3,stroke:#657b83
  style B fill:#eee8d5,stroke:#657b83
  style C fill:#fdf6e3,stroke:#657b83
  style D fill:#eee8d5,stroke:#657b83
  style E fill:#fdf6e3,stroke:#657b83
  style F fill:#eee8d5,stroke:#657b83
  style G fill:#fdf6e3,stroke:#657b83
  style H fill:#eee8d5,stroke:#657b83
  style I fill:#fdf6e3,stroke:#657b83
  style J fill:#eee8d5,stroke:#657b83
  style K fill:#fdf6e3,stroke:#657b83
  style L fill:#eee8d5,stroke:#657b83
  style M fill:#fdf6e3,stroke:#657b83
  style N fill:#eee8d5,stroke:#657b83
  style O fill:#dc322f,stroke:#657b83,color:#fff
  style P fill:#b58900,stroke:#657b83,color:#fff

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