400+
Native connectors
MetadataOps Kit
MetadataOps
Every claim about grounding an agent, defending a number, or answering an auditor rests on the same foundation: whether the metadata describing your estate is complete, deep, derived, current, and governed.
MetadataOps is the discipline of running your metadata estate as an operated system, and the role that owns it. Build your practice by combining people, process, and technology.
The discipline
What MetadataOps is
A data engineer moves the data. A MetadataOps engineer owns what it means, everywhere it lands.
The work sits between data engineering, data governance, and AI platform engineering, and it borrows its operating discipline from all three. There's a build, a test, a deploy, a diff, and a rollback.
MetadataOps is the supply side. Context engineering is what happens to that supply at the moment an agent asks.
Upstream
Supply
MetadataOps
Decides what exists to be retrieved. Metadata harvested across the estate, modeled into owned definitions, versioned, and governed, so there is something trustworthy to assemble from.
Downstream
Retrieval
Context engineering
Decides which of it reaches an agent's window. Which definitions, which policies, which lineage, in what order, under what token budget. It can only reach what the supply side already collected.
The lifecycle
Five stages, run as operations
Each stage depends on the one before it. Skip one and the failure surfaces downstream, as a number two teams can't reconcile or an agent answer that can't be traced to its source.
Harvest
Databases · Code · BI · Cloud
Collect metadata from every system that touches the data, from legacy databases and transformation code to BI tools and cloud platforms. Coverage sets the ceiling for every stage after it.
Model
Define · Own · Bind
Define each business concept once, give it a named owner, and bind it to the physical assets that implement it. A definition with no owner and no asset behind it is a suggestion.
Version
Log · Attribute · Promote
Handle a definition change the way you'd handle a code change. Every edit is logged, attributed, reversible, and promoted through dev, test, and production before anything downstream depends on it.
Compile
Native formats · Downstream
Write governed definitions into the native formats of warehouses, BI tools, and agents that consume them. Each consumer reads the same definition in a form it already understands.
Verify
Drift · Impact · Evidence
Confirm the estate still matches the record. Catch drift, analyze impact before a schema change ships, and keep the evidence trail ready for the day someone asks where a number came from.
Measurement
How to measure metadata quality
Dimension
The question
Coverage
What share of the estate has harvested metadata at all?
Resolution
Column-level or table-level?
Derivation
Parsed from code, or inferred from run logs?
Freshness
Does the record match the live estate today?
Governance state
Is each definition owned, versioned, policy-bound, and tied to a physical asset?
Where the boundary sits
These five describe the metadata, never the data values. Bad values, null rates, data recency, and pipeline reliability belong to data quality tooling and data engineering. A strong score says the estate is well described. It says nothing about whether last night's load was correct.
Four states of a metadata estate
0 to 6
Described
Metadata is documentation. It supports discovery and human judgment. Agents produce confident answers with no way to check them.
7 to 12
Partial
The modern slice is in reasonable shape and the edges are dark. Agents are right about the covered path and wrong in ways that are hard to predict.
13 to 16
Traceable
You can answer where a number came from across most of the estate, with named exceptions. The first band where grounding claims survive a motivated technical challenge.
17 to 20
Operated
The estate runs as a system. An agent grounds on a governed definition, and an auditor gets a query result on demand.
Built for the practice
MetaKarta is a metadata management platform built on a shared metadata repository, with 400+ native connectors, parser-based column-level lineage, and full version and configuration management of the metadata estate across dev, test, and production.
The five dimensions above describe what the practice requires. The platform is how we built for it.
~30 years
OEM metadata expertise