Unified data discovery with business context in Unity Catalog

2026-03-04 · Source: Databricks · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Cloud Computing & IT Infrastructure · Depth: Intermediate, quick

Summary

Databricks has introduced a new "Discover" experience, now in Beta and built directly into Unity Catalog, to address the challenge of fragmented data discovery and help organizations find the right, trusted data and AI assets with business context. This unified platform replaces tool-specific searches by embedding business meaning through "Domains" and governed metadata, surfacing trust signals via certifications and deprecations, and leveraging platform-native data intelligence like usage and lineage. The "Discover" page spans the entire lakehouse, including structured and unstructured data, dashboards, notebooks, and AI assets, all through a single, governed experience. "Domains" further organize assets by business unit or use case, allowing flexible curation across multiple domains without duplication, thereby making discovery intuitive and aligned to business needs. This initiative aims to improve data adoption, reduce wasted time, and streamline access workflows for data and AI assets.

Key takeaway

Databricks' new "Discover" experience, integrated into Unity Catalog, unifies fragmented data, analytics, and AI asset discovery by embedding business context directly. Now in Beta, it leverages "Domains" for flexible, business-aligned asset organization and uses platform intelligence (usage, lineage) with governed metadata to surface trusted assets across the entire lakehouse. This empowers AI/ML professionals to quickly find and understand relevant data and AI assets, accelerating development and ensuring data trust.

Topics

• Databricks
• Unity Catalog
• Data Discovery
• Metadata Management
• Data Governance

Best for: CTO, VP of Engineering/Data, Director of AI/ML, Data Scientist, Data Engineer, AI Product Manager

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Editorial summary, takeaway, and curation by AIssential. Original article published by Databricks.