Freshness, volume, schema & drift monitoring
Most quality failures are ordinary. A feed lands late, a file arrives half-empty, or an upstream system renames a column. Monitoring covers those baselines on every connected dataset, spanning freshness, volume, schema, and drift, without anyone writing a rule or calling a meeting. And a check that failed to run counts as a failure, never a silent green.
Freshness is tracked against each dataset's expected cadence, so a late feed shows as late before any report consumes stale data.
Volume checks flag a load arriving well outside its normal range, so the half-empty file gets caught on arrival, at the source.
Schema change detection flags renamed, dropped, or retyped columns the moment they land, with the downstream reports already mapped.
Distribution drift monitoring spots a column quietly changing shape over weeks, which no single-load check would ever see.
Absence is failure. A check that did not run reports as a failure, so a dead monitor can never masquerade as a healthy dataset.
Every result, pass or fail, is sealed on the ledger, so the claim that the feed was on time all quarter is a provable statement rather than a recollection.
Baseline
The platform learns each dataset's normal cadence, volume, and shape from its own history.
Watch
Every load is checked against that baseline where the data lives, and only metadata comes back.
Flag
A miss opens a finding tied to the dataset and to every downstream report on the graph.
Seal
Each result lands on the hash-chained ledger, so the monitoring record itself stands up in an exam.
“Was this feed on time every day this quarter, and can you prove it rather than just assert it?”
It can be proven. Freshness is tracked against the feed's expected cadence on every load, and every result, pass or fail, is sealed on the hash-chained ledger, so on-time delivery is a provable statement backed by the sealed record.
“If a source system changes a schema, who finds out, and when?”
Schema change detection flags renamed, dropped, or retyped columns the moment they land. The finding is tied to the dataset and to every downstream report on the graph, so the accountable owners see it immediately.
“What is watching the datasets nobody thought to write rules for?”
Monitoring covers every connected dataset by default. The platform learns each dataset's normal cadence, volume, and shape from its own history and checks every load against that baseline, with no rule authoring required.
Start with proof, not a promise.
There are three ways in, and each one earns the next. You approve the exit criteria before we spend any more of your time or budget.
A 60-minute working session
Bring one report that is expensive to prove. We walk the mechanism against it. Nothing is required from your side.
A two-week guided sandbox
Your team works hands-on against a synthetic sample bank. It requires zero access to your systems and zero procurement.
A six-week proof on your systems
We use read-only metadata across five systems and one priority report, scored against criteria both sides sign first. The decision it produces is yours.