Introducing Conduktor Trust: in-stream data quality enforcement

Bad data (missing fields, broken formats, out-of-range values) breaks downstream systems, corrupts AI outputs, and causes customer-facing issues. Traditional tools fix data after the damage. Data contracts push responsibility to producers with fragmented enforcement.

Conduktor Trust enforces data quality in-stream, before bad data reaches your pipelines.

How Trust works

1. Define rules: Use CEL (Common Expression Language) to specify structure, completeness, and conformance
2. Apply policies: Attach rules to specific topics and control where enforcement applies
3. Act on violations: Log violations for monitoring or block bad messages instantly
4. Track patterns: View violations over time to identify recurring issues

What Trust prevents

• Data quality issues reaching downstream: Catch problems at the source
• Bad data in AI models: Feed only trusted data to models and analytics
• Compliance gaps: Apply consistent quality policies across teams
• Reactive cleanup work: Fix issues before they spread

For a full list of changes, read the complete release notes.