Enterprise Metadata Management System

Solution Architecture v1.0 · Data & AI Global Practice · 2026-08 · 22 views

A metadata platform architected as a connected knowledge layer rather than a database of table and column descriptions. The value is in the relationships — between data assets, business concepts, people, policies, quality and lineage — so the architecture is organised around a canonical model with versioned aspects, one authoritative store with disposable projections, a precedence rule that protects curated metadata from being overwritten by a harvest, and an activation path that pushes classifications back out to the systems that enforce them.

22 views 22 HTML views22 SVG22 draw.io Updated 2026-08-27
Architecture views

22 views, each in three formats.

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  1. 01
    System Context

    Who depends on the platform, what it harvests from, and what it pushes back out.

  2. 02
    High-Level Architecture

    The six stages a piece of metadata passes through, from a source system to a person who trusts it.

  3. 03
    Layered Architecture

    The dependency rule: which layer is allowed to call which, and where the cross-cutting concerns sit.

  4. 04
    Container Architecture

    The deployable units, grouped by responsibility, and the calls that cross between them.

  5. 05
    Integration Architecture

    Every interface in and out, and the rule that keeps the platform from becoming another silo.

  6. 06
    Connector Coverage

    What each class of source can actually give up, and where the honest gaps are.

  7. 07
    Canonical Metadata Model

    What counts as an asset, how it relates to people, terms, policy and quality, and how it is extended.

  8. 08
    Storage Architecture

    Which store is authoritative, which stores are disposable, and what that split buys.

  9. 09
    Metadata Flow and Precedence

    The path from capture to activation, and the rule applied when two systems disagree.

  10. 10
    Harvest and Conflict Resolution

    One incremental run end to end, including the case where the source contradicts a steward.

  11. 11
    Lineage Construction

    How four kinds of evidence become column-level edges, and what happens when they cannot.

  12. 12
    Search and Discovery

    The read path a data consumer actually experiences, and where entitlements are applied.

  13. 13
    Stewardship Workflows

    How a gap becomes an owned, reviewed, published definition — through the four personas in turn.

  14. 14
    Drift and Impact Analysis

    What happens between a column being dropped upstream and the report that breaks because of it.

  15. 15
    Data Quality Metadata

    How quality results reach the catalog without the catalog becoming a quality engine.

  16. 16
    Deployment and Infrastructure

    Where it runs, how it survives an availability-zone loss, and how it reaches isolated networks.

  17. 17
    Delivery and Extensibility

    How a new connector, a new asset type and a platform change each reach production.

  18. 18
    Observability and Operations

    What is measured, and which signals are allowed to wake somebody up.

  19. 19
    The Active Metadata Loop

    The cycle that has to close for this to be a governance platform rather than a search index.

  20. 20
    Security Trust Zones

    Where the boundaries are, what authenticates at each crossing, and why metadata is treated as sensitive.

  21. 21
    Authorisation and Audit

    One governance action end to end, proving that both permitted and denied outcomes are recorded.

  22. 22
    Failure Modes and Recovery

    What is allowed to fail, what is not, and the risks that are being accepted with their eyes open.

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