Continuously knowing the health of your data — freshness, volume, schema and values — so problems surface before users do.
Reviewed by Francisco Ferreira ·
Data observability is the practice of always knowing whether your data is healthy, instead of finding out from an angry stakeholder. It watches five things: freshness (is it up to date?), volume (did the row count move as expected?), schema (did columns change?), distribution (do the values still look normal?) and lineage (what feeds what?).
It borrows the idea from software monitoring: you don't wait for a crash, you watch signals that warn you first. Applied to data, it means catching a broken load or a null spike the morning it happens — not at quarter-end, after the wrong number has already shipped.
Tabkeel makes data observability something you don't need a data team to run: connect read-only, let it learn baselines, get alerted when a signal drifts. It fits teams with a data engineer and teams without one.