DATA OPS

Stale Table Upstream Root-Cause Tracer

When a freshness alert fires, an agent walks the table's upstream lineage and recent job logs to pinpoint which dependency or load job actually broke.

CategoryData Ops
Enginepaperclip
Difficultyadvanced
Triggerwebhook
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerWebhook: freshness breach receivedHTTP webhook
  • ActionFetch upstream lineage + run historySnowflakeSnowflake
  • ActionPull load-job logs and failuresAxiom
  • LogicIdentify earliest broken hop and failure class
  • OutputFile Linear issue with root-cause hypothesisLinearLinear

What it does

Given a table that missed its load window, this agent traces the dependency chain backward through Snowflake lineage and the ingestion job logs in Axiom to find the earliest failing or stalled step. It distinguishes "upstream source never arrived" from "transform job errored" from "job ran but produced zero rows," then opens a Linear issue with the diagnosis and supporting evidence.

When to use it

Use it when freshness breaches are frequent enough that manually tracing each one through lineage and logs eats your on-call's morning. It does the first hour of investigation automatically.

How it works

  1. 1A freshness-breach webhook delivers the stale table name and its SLA lag.
  2. 2The agent pulls the upstream lineage and recent run history from Snowflake.
  3. 3It queries Axiom for the corresponding load-job logs and failure signatures.
  4. 4It reasons over the chain to identify the earliest broken hop and the failure class.
  5. 5It files a Linear issue with the root-cause hypothesis, evidence links, and severity.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect SnowflakeWarehouses, queries, shares.
  2. 2
    Connect AxiomLog streams, queries, dashboards.
  3. 3
    Connect LinearIssues, projects, cycles, triage.
  4. 4
    Connect HTTP webhookTrigger any URL on agent actions.
  5. 5
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  6. 6
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  7. 7
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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