Reconciliation, not guesswork
Data observability tells you something looks wrong. DataRecs proves what is wrong — down to the exact row and cell.
Probabilistic monitoring is not proof
Data observability and anomaly-detection tools use machine learning to flag when a table looks unusual — freshness slipping, volume spiking, schema drifting. That's genuinely useful for catching surprises across a warehouse. But it's probabilistic: it raises a suspicion, not a verdict.
Reconciliation asks a harder, more concrete question: does the target match the source, exactly? DataRecs compares the actual values, row by row and column by column, and returns the precise rows and columns that differ. Not a confidence score — the evidence.
Two different jobs
Both have their place. Here's how they differ.
| Data observability | DataRecs reconciliation | |
|---|---|---|
| Detection method | ML anomaly detection (probabilistic) | Value-level comparison (deterministic) |
| Answer you get | “This table looks unusual” | “These 47 rows and 3 columns differ, here they are” |
| Defensible to an auditor | Hard to explain a model's confidence score | Exact, reproducible evidence |
| Best for | Broad monitoring of a warehouse | Proving two systems agree |
| Cross-system | Usually within one warehouse | Across different database engines (Postgres, Oracle, DB2, SQL Server, MySQL) |
Where reconciliation is the right tool
When "probably fine" isn't good enough and you have to prove it.
Post-ETL validation
Confirm that what landed in the target matches what left the source after every pipeline run.
Migration cutover
Prove parity before you switch systems off — down to the exact mismatched values.
Cross-system financial reconciliation
Show that ledgers, sub-ledgers, and downstream systems agree, with auditable evidence.
Regulatory reporting
When you must prove the numbers agree, produce exact, reproducible evidence.
You still get the monitoring
Choosing certainty doesn't mean giving up the operational surface. DataRecs runs on a schedule, alerts you when something breaks, and lets you drill into exactly what changed — without the false confidence of a black-box model.
- Scheduled reconciliation runs
- Email and HMAC-signed webhook alerts
- Drill-down discrepancy reports
- A full audit trail of every run
Stop guessing. Start proving.
Connect a source and a target and watch DataRecs surface the exact rows and columns that differ — on your own data.