Knowledgebase

Monitoring Data Pipelines Print

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Knowing they worked.

WHAT TO MONITOR

Whether each run completed Duration, against normal Row counts produced

Freshness: how old the newest data is

Quality test results Cost, where usage-billed

WHY FRESHNESS MATTERS MOST TO CONSUMERS

A dashboard showing stale data without saying so is worse than one that is unavailable.

WHAT TO SURFACE TO USERS

When the data was last updated.

WHAT TO ALERT ON

Failure Freshness exceeding a threshold Row counts outside expected bounds Quality tests failing Duration far above normal

WHY ROW COUNTS SPECIFICALLY

Successful runs producing zero or half the usual rows are the commonest silent failure.

WHAT TO SET BOUNDS FROM

Historical values, rather than fixed guesses.

WHAT NOT TO ALERT ON

Anything nobody will act on.

WHAT TO REVIEW

Alerts that fired and were ignored.

WHAT LINEAGE PROVIDES

Knowing what a table depends on, and what depends on it.

WHY IT MATTERS OPERATIONALLY

It answers who is affected when something fails, immediately.

WHAT TO GENERATE IT FROM

The transformation definitions, automatically.

WHAT TO COMMUNICATE ON FAILURE

Who is affected, and when it will be fixed.


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