DATA OPS
dbt Row-Volume Z-Score Anomaly to Linear Ticket
Compares each day's row counts for tracked Snowflake tables against their trailing 30-day baseline.
How it runs
The automated pipeline, trigger to output.
- TriggerSchedule after nightly load
- ActionQuery today and 30-day counts from SnowflakeSnowflake
- LogicCompute z-score and flag deviations
- LogicSuppress known low-volume tables
- ActionFile Linear issue per anomalyLinear
- OutputEmit summary of issues opened
What it does
After the nightly load, this workflow pulls today's row counts for a configured set of Snowflake tables and computes a z-score against each table's trailing 30-day mean and standard deviation. Tables whose volume deviates beyond the threshold become anomalies, and each one is filed as a Linear issue routed to the owning team with the numbers attached.
When to use it
Use it when row volume is your earliest signal of an upstream break — a half-empty fact table or a duplicate-driven spike usually means a pipeline regression hours before any dashboard looks wrong. Ideal for data teams that already triage in Linear.
How it works
- 1A schedule fires after the nightly dbt run completes.
- 2Snowflake returns today's row count plus the 30-day window per tracked table.
- 3A logic step computes the z-score and flags tables outside the band.
- 4A branch suppresses known low-volume tables to cut noise.
- 5For each surviving anomaly, a Linear issue is created with current vs baseline counts, the z-score, and a suggested owner label.
- 6The flow outputs a summary of issues opened for the run log.
Set it up
What you configure once, before turning it on.
- 1Connect SnowflakeWarehouses, queries, shares.
- 2Connect LinearIssues, projects, cycles, triage.
- 3Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 4Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 5Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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