Datapace documentation
Datapace provides database teams with one tool to understand, run, and govern their databases across engines, and AI agents to improve their database workflows. These pages explain how it works, in the order a team meets it.
Getting started
- IntroductionWhat Datapace is, who it is for, and what these docs cover: one graph of what your databases mean and what runs them, agents that propose, your team approving.
- How it worksThe Datapace loop in five steps: connect within an agreed scope, infer, confirm with your experts, agents propose, your team approves and records.
- Docs for LLMsMachine-readable exports of these docs: a Markdown twin of every page, the site index at llms.txt, and the Copy page button, for agents and retrieval.
Concepts
- The graphOne graph per estate: what each database means (entities, measures, relationships, lineage) and what runs it (instance class, storage, cost, usage, freshness).
- Infer and confirmWhat Datapace reads, what it infers with a confidence score, inferred versus validated, and how a few working sessions turn proposals into a confirmed model.
- AgentsThe five Datapace agents (FinOps, Performance, Quality, Migration, Documentation), what each proposes on the graph, what a proposal contains, who approves.
- Policy, approval, and auditEvery action goes through policy, the risky ones wait for a person, and all of it lands in one audit ledger: who, what, where, evidence, approver, outcome.
- Governed contextHow copilots, BI, and MCP clients reach production data through the confirmed graph and its gate instead of raw access, and what travels with each column.
- Across enginesDatapace is engine-agnostic by design. The signals it reads differ per engine; the graph, the agents, the policy, and the ledger are the same for all.
Guides
- Documenting an estateHow a team takes an inherited estate to a confirmed graph: agree the scope, run the first inference, hold the confirmation sessions, keep the model current.
- Reviewing a proposalHow to read a proposal from a Datapace agent: the change, the evidence, the affected objects, the risk, and what approving or rejecting records.
- Migration mappingFrom legacy discovery to a validated mapping: map the source, let the Migration agent propose candidates, have experts validate, find problems before load.
Reference
- GlossaryThe vocabulary these docs use, one definition each: graph, entity, measure, lineage, confidence score, proposal, evidence, approval, ledger, governed context.
- Frequently asked questionsThe questions teams ask first: does anything run on its own, does Datapace move data, who validates, does it replace a catalog, what about compliance.
For agents
Every page is also served as Markdown at its URL with a .md suffix, and llms.txt lists them all with the rest of the site.
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