DATA OPS

Replay Quarantined Reject Rows from S3 into Salesforce

Scans an S3 quarantine bucket of previously rejected reverse-ETL rows, retries each against Salesforce.

CategoryData Ops
Enginesim
Difficultyadvanced
Triggerschedule
Steps6
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerScheduled replay sweep every few hours
  • ActionList pending reject files in S3 quarantine prefixAWS S3
  • ActionRetry upsert of each row to SalesforceSalesforce
  • LogicBranch success vs still-failing and check attempt cap
  • ActionArchive successes and rewrite failures with attempt count in S3AWS S3
  • OutputPost max-attempt rows to Slack for manual reviewSlack

What it does

Failed reverse-ETL rows often get dumped as JSON files into an S3 reject bucket and forgotten. This workflow drains that backlog: it reads each quarantined file, retries the write to Salesforce, archives the ones that now succeed, and keeps the rest with an updated attempt counter so they don't loop forever.

When to use it

Run this on a schedule after your team fixes data-quality issues upstream (deduped accounts, corrected picklist values). It clears the reject backlog without anyone hand-replaying files, and it surfaces rows that have exhausted their retries.

How it works

  1. 1A schedule triggers the replay sweep, e.g. every few hours.
  2. 2The flow lists pending reject files under the S3 quarantine prefix.
  3. 3For each row it attempts an upsert to the matching Salesforce object.
  4. 4A branch separates rows that now succeed from rows that still fail.
  5. 5Successful rows are moved to an archive prefix and removed from the quarantine prefix.
  6. 6Still-failing rows are rewritten with an incremented attempt count; those past the max-attempt limit are posted to a Slack channel for manual review.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect AWS S3Buckets, objects, signed URLs.
  2. 2
    Connect SalesforceAccounts, opportunities, cases.
  3. 3
    Connect SlackChannels, DMs, threads, mentions.
  4. 4
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  5. 5
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  6. 6
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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