DATA OPS
Reverse-ETL Webhook-Triggered Batch Landing Audit
When a reverse-ETL tool fires its post-run webhook, immediately audits that the run's claimed row count matches the count actually present in Salesforce and pages on-call if…
How it runs
The automated pipeline, trigger to output.
- TriggerReverse-ETL completion webhook receivedHTTP webhook
- ActionCount actual Salesforce records for batch windowSalesforce
- LogicCompute claimed-vs-actual variance
- LogicBranch on variance threshold
- ActionLog result to AxiomAxiom
- OutputPage on-call via PagerDuty if over thresholdPagerDuty
What it does
Turns a reverse-ETL tool's completion webhook into an instant landing audit. The webhook reports how many rows the run wrote; this workflow independently re-counts the affected records in Salesforce for the same batch window and compares. A small variance is logged; a variance above your tolerance pages the on-call data engineer through PagerDuty so a bad run is caught within minutes, not the next morning.
When to use it
Use it when your reverse-ETL platform can fire a webhook on run completion and you treat Salesforce as a system of record where missing rows have downstream revenue impact. This is the fast-feedback complement to nightly reconciliation: it catches catastrophic partial failures while the run context is still fresh.
How it works
- 1The reverse-ETL tool's completion webhook triggers the run, carrying the batch ID and claimed row count.
- 2Query Salesforce for the actual count of records touched in that batch window.
- 3Compute the variance between claimed and actual counts.
- 4If variance is within tolerance, log the result to Axiom and stop.
- 5If variance exceeds the threshold, open a PagerDuty incident with the batch ID and the gap.
Set it up
What you configure once, before turning it on.
- 1Connect HTTP webhookTrigger any URL on agent actions.
- 2Connect SalesforceAccounts, opportunities, cases.
- 3Connect AxiomLog streams, queries, dashboards.
- 4Connect PagerDutyIncidents, on-call, escalations.
- 5Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 6Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 7Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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