DATABRICKS APP
Data Quality
Manager
Define, run, and monitor data quality validation rules directly against Unity Catalog tables — from a browser UI, without writing a single line of code.
Data quality is not a one-time project. It is a continuous practice — applied at every layer of your Medallion architecture, every time data moves.
THAT'S WHY WE BUILT THIS
CAPABILITIES
Everything you need to govern data quality
One app covers the full lifecycle — from defining expectations to monitoring results — all within your existing Databricks workspace.
Visual expectation editor
Define data quality rules through a point-and-click UI. 27+ built-in expectation types cover row counts, null checks, value ranges, regex patterns, and more — no Python required.
Job-based validation
Trigger validation runs as Databricks Jobs on proper cluster compute. Results appear on the dashboard in real time — the UI stays responsive while validation runs in the background.
Results dashboard
View pass/fail trends over time with interactive charts. Drill into individual expectation results, compare runs, and identify which tables or columns need attention.
Unity Catalog browser
Browse your entire catalog hierarchy — catalogs, schemas, tables, and columns — directly in the app. Read and write table and column comments without leaving the UI.
Failure notifications
Automatically alert your team when validation fails. Supports Microsoft Teams webhooks and email via SMTP. Per-suite subscriber lists keep the right people informed.
Secure by design
All data stays in your Databricks workspace. The app runs on your infrastructure, uses OAuth-based authentication automatically, and stores all data in Unity Catalog Delta tables you control.
WHERE IT FITS
A quality gate for your Medallion architecture
The Data Quality Manager sits alongside your existing data pipelines. It validates data after each layer load — Bronze, Silver, and Gold — catching issues before they propagate downstream.
Catch ingestion issues immediately — before raw data enters the lake.
- Row count validation
- Schema conformance
- File completeness
Ensure standardised tables meet business rules before powering analytics.
- Null checks on key columns
- Value range constraints
- Referential integrity
Guard data products and dashboards with final-layer quality gates.
- KPI metric bounds
- Aggregate completeness
- Cross-table consistency
GETTING STARTED
Up and running in under an hour
No infrastructure to provision beyond what you already have. If you have a Databricks workspace with Unity Catalog, you're ready.
01
Install from the Marketplace
Find the Data Quality Manager listing on the Databricks Marketplace and install it into your workspace with a single click.
02
Grant catalog permissions
Grant the app service principal CREATE SCHEMA and USE CATALOG access. The app automatically creates the dataquality schema and Delta tables on first startup.
03
Configure the app
Set the CATALOG environment variable and attach a SQL Warehouse resource. The app handles the rest automatically.
04
Define your first suite
Open the app, navigate to Define Checks, create an expectation suite for any Unity Catalog table, and add expectations from the built-in catalogue.
05
Run validation
Click Run Validation to trigger a Databricks Job. Results appear on the dashboard within minutes — with Teams or email alerts if anything fails.
BUILT ON
Powered by industry-leading platforms
No proprietary lock-in. Built on open standards and the platforms your team already knows.
PLATFORMGET STARTED
Ready to add data quality to your Databricks platform?
Install from the Databricks Marketplace or reach out to the Plainsight team for a guided setup.