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Databricks Genie lets teams ask natural language questions about enterprise data and get real answers. Understanding what data those conversations relied on, and whether it was accurate, classified, and governed, has remained a separate problem.
Agent Trust Hub now supports Databricks as a connected source. Teams using Databricks Genie can connect their environment to Agent Trust Hub and bring Genie agent activity into a central registry alongside the data trust signals Bigeye's platform provides.
What becomes visible when you connect Databricks:
- Genie conversations and agent activity in the Agent Trust Hub registry
- The data accessed during each conversation, mapped to lineage and ownership context
- Data quality and classification signals for the tables and fields Genie relied on
- Usage and cost signals associated with Genie activity
- Governance context: open tickets, policy flags, and data ownership for accessed data
For teams running Databricks Genie in enterprise workflows, the connector answers a question that agent activity logs alone can't: was the data behind that conversation trustworthy?
Agent Trust Hub is available now. Start here.
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Resource
Monthly cost ($)
Number of resources
Time (months)
Total cost ($)
Software/Data engineer
$15,000
3
12
$540,000
Data analyst
$12,000
2
6
$144,000
Business analyst
$10,000
1
3
$30,000
Data/product manager
$20,000
2
6
$240,000
Total cost
$954,000
Role
Goals
Common needs
Data engineers
Overall data flow. Data is fresh and operating at full volume. Jobs are always running, so data outages don't impact downstream systems.
Freshness + volume
Monitoring
Schema change detection
Lineage monitoring
Monitoring
Schema change detection
Lineage monitoring
Data scientists
Specific datasets in great detail. Looking for outliers, duplication, and other—sometimes subtle—issues that could affect their analysis or machine learning models.
Freshness monitoringCompleteness monitoringDuplicate detectionOutlier detectionDistribution shift detectionDimensional slicing and dicing
Analytics engineers
Rapidly testing the changes they’re making within the data model. Move fast and not break things—without spending hours writing tons of pipeline tests.
Lineage monitoringETL blue/green testing
Business intelligence analysts
The business impact of data. Understand where they should spend their time digging in, and when they have a red herring caused by a data pipeline problem.
Integration with analytics toolsAnomaly detectionCustom business metricsDimensional slicing and dicing
Other stakeholders
Data reliability. Customers and stakeholders don’t want data issues to bog them down, delay deadlines, or provide inaccurate information.
Integration with analytics toolsReporting and insights
Bigeye Staff
Get the Best of Data Leadership
Subscribe to the Data Leaders Digest for exclusive content on data reliability, observability, and leadership from top industry experts.
Want the practical playbook?
Join us on April 16 for The AI Trust Summit, a one-day virtual summit focused on the production blockers that keep enterprise AI from scaling: reliability, permissions, auditability, data readiness, and governance.
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Join hundreds of data professionals who subscribe to the Data Leaders Digest for actionable insights and expert advice.