Reverse ETL Failure Modes and Edge Cases

Silent Data Inconsistencies: The most dangerous failures are the ones you do not notice. APIs can partially succeed: 9,500 records write successfully, but 500 fail due to validation errors like malformed phone numbers or missing required fields. If your observability is weak, sales teams spend weeks making decisions on incomplete data before someone realizes accounts are missing churn scores. This gets worse at scale. Syncing 5 million records daily with a 0.1% error rate produces 5,000 incorrect records per day. Over a month, that is 150,000 bad records silently polluting your CRM. The failure is not catastrophic enough to page anyone, but it is large enough to materially harm business operations. Idempotency Failures and Side Effects: Many destination APIs trigger side effects on writes. Updating a lead score in a CRM might automatically reassign the lead to a different sales rep or trigger an email workflow. If your sync retries without proper idempotency, you can create chaos. Consider a sync processing 1 million records that fails midway and retries 3 times without idempotence keys. That is 3 million writes instead of 1 million. If each write triggers an email, you just sent 2 million duplicate emails to customers. If each write increments a counter, your metrics are now triple counted. Production systems must use version checks, idempotency tokens, or compare timestamps before writing. Identity Resolution Breaks: Most Reverse ETL systems match records using keys like email addresses or external IDs. This works until it does not. If you match by email and a user changes their email, you might create a duplicate record in the destination instead of updating the existing one. Now you have two CRM contacts for the same person. Backfills are especially dangerous. If you reprocess historical data and your warehouse primary keys change (common when rebuilding dimension tables), the mapping between warehouse IDs and destination IDs breaks. Your sync might create thousands of orphan records or fail to update existing ones. The fix requires maintaining stable external IDs that survive backfills or implementing fuzzy matching logic. Schema Drift: Warehouses and destination APIs both evolve. Your warehouse team renames a column from user_score to churn_risk_score. Your sync breaks because the transformation layer still maps the old column name. Worse, Salesforce deprecates a custom field you are writing to. Your sync fails silently or writes to the wrong field. Production systems need schema validation on both sides. Before running a sync, check that expected columns exist in the warehouse and required fields are writable in the destination. Tools like Census and Hightouch provide schema drift detection, but it is not foolproof. Strong data contracts and change management processes are essential. Regulatory Edge Cases: When a user requests deletion under General Data Protection Regulation (GDPR) or California Consumer Privacy Act (CCPA), deleting from the warehouse is insufficient. Reverse ETL must propagate deletions or opt out flags to all destinations. If you only sync positive data and ignore hard deletes, stale user records remain in your CRM and marketing tools, keeping you in violation. Implementing compliant deletion requires tracking which records were synced to which destinations, then issuing delete API calls to each. Some destinations lack delete APIs entirely, forcing manual cleanup. The complexity multiplies with each new destination you add.