Detect bad data before it reaches dashboards
A single malformed record in a source system becomes a broken dashboard, a wrong forecast, and an embarrassing email from the CFO asking why the numbers changed overnight.
How the Data Quality works
Define your quality rules: expected ranges, null tolerances, referential integrity constraints, and freshness requirements. The agent evaluates every batch against those rules and flags failures before bad data propagates to reports.
Quality checks it performs:
- Validates value ranges and data types against expected schemas
- Detects sudden distribution shifts that indicate upstream problems
- Checks referential integrity across related tables
- Measures freshness against defined SLAs
Why you need the Data Quality
Finance, healthcare, and any domain where wrong numbers create compliance risk or financial exposure. If a data error could show up in a regulatory filing, this agent belongs in your stack.
How the Data Quality compares
The Log Analysis monitors application behavior through logs. The Data Quality Agent monitors data accuracy in your warehouse. Different signals, same goal: catching problems before users do.
