DATA OPS
Snowflake to HubSpot Sync: Quarantine Rejects and Replay on Fix
Detects rows HubSpot rejected during a Snowflake reverse-ETL push, parks them in a quarantine table with the exact error.
How it runs
The automated pipeline, trigger to output.
- TriggerNightly schedule after Snowflake export materializes
- ActionRead new and previously-quarantined rows from SnowflakeSnowflake
- ActionUpsert each row to HubSpot contacts APIHubSpot
- LogicBranch accepted vs rejected by response code
- ActionWrite rejected rows to Snowflake quarantine table with errorSnowflake
- OutputClear and confirm replayed rows that now validateSnowflake
What it does
When a reverse-ETL job pushes Snowflake records into HubSpot, some rows fail validation (bad email, missing required property, enum mismatch). This workflow captures each rejected row individually instead of failing the whole batch, writes it to a `retl_quarantine` table in Snowflake with the precise HubSpot error message, and replays the row automatically once it passes validation again.
When to use it
Run this when your nightly customer or contact sync to HubSpot silently drops records and your CRM falls out of date. It turns opaque batch failures into a per-row audit trail your data team can actually act on.
How it works
- 1A schedule kicks off after the nightly Snowflake export materializes.
- 2The flow reads new rows from the export view in Snowflake.
- 3For each row it upserts to the HubSpot contacts API and inspects the response code.
- 4A branch splits accepted rows from rejected ones (4xx with a property error).
- 5Rejected rows are written to the Snowflake quarantine table with the error and a retry counter.
- 6On the next run, previously quarantined rows whose data now validates are replayed and cleared from quarantine.
Set it up
What you configure once, before turning it on.
- 1Connect SnowflakeWarehouses, queries, shares.
- 2Connect HubSpotCRM, deals, marketing, support.
- 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.
More Data Ops workflows
BigQuery Per-Team Budget Breach Alert to PagerDuty
Tracks month-to-date BigQuery scheduled-query spend per team and, when a team crosses its monthly budget, pages the team's on-call in PagerDuty and snapshots the spend breakdown…
dbt orphan model detector with Linear cleanup tickets
Scans your dbt manifest for models that no other model, exposure, or BI tool consumes.
Weekly BigQuery Cost Trend Sheet and Exec Digest
Compiles week-over-week BigQuery scheduled-query cost by owner and dataset into a Google Sheet with trend columns.
Backfill Missing Owner Labels on BigQuery Scheduled Queries
Finds scheduled queries with no owner label, infers the likely owner from creator metadata and target-table lineage, proposes a label.
Daily BigQuery Scheduled-Query Cost Attribution to Owners
Each morning, totals the prior day's on-demand bytes-billed per scheduled query, maps each query to its owner from a label, and posts a per-owner cost leaderboard to Slack.
dbt source freshness watcher with severity-routed alerts
Checks Snowflake loaded-at timestamps against each dbt source's freshness SLA, then routes warnings to Slack and hard breaches to a PagerDuty incident so stale data never…
Run it inside a business
This workflow drops into a full company template. Import the org, and this is one of the playbooks its agents run.
Run this workflow in your colony.
14-day trial. No DevOps. No Sales call. Provisioned in under a minute.