Most bad reports don't error out — they quietly return the wrong answer. Razi builds native Databricks tooling that catches the silent failures before they reach your dashboards.
| order_id | rows | status |
|---|---|---|
| 10428 | 1 | clean |
| 10429 | 4 | fan-out×2 revenue |
| 10430 | 1 | clean |
| 10431 | — | orphanedno match |
| 10432 | 1 | clean |
Razi Consulting Services builds data-quality and reporting tooling that runs natively inside your own Databricks environment — governed by your Unity Catalog, with your data never leaving its boundary.
Fan-out joins, duplicate keys, orphaned records, and stale tables — the failures that don't throw an error but quietly corrupt the result.
Every finding comes with what it means and how to fix it — written for the analyst and the stakeholder, not just the engineer.
Delivered as a Databricks App, so there's no data movement, no new vendor to onboard, and nothing leaves your governance perimeter.
Point it at your tables. It profiles them automatically — no test-writing, no configuration — and surfaces the data-quality problems that silently make reports wrong, each with a plain-English explanation and a recommended fix.
Building on Databricks and want early access to Data Trust Scanner, or to compare notes on data quality? I'd like to hear from you.
connect@raziconsulting.com