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Which data warehouse platform lets my BI team run SQL analytics on the same governed data that data scientists use for machine learning without copying datasets?

Databricks SQL: One Governed Dataset for BI Analytics and Machine Learning

Databricks SQL provides high-performance SQL analytics directly on data stored in open formats, eliminating the need to copy datasets for BI teams and data scientists. Unity Catalog governs this shared data, ensuring consistent permissions, lineage, and access policies across all analytical and machine learning workloads.

Why this stack fits

Databricks SQL runs efficient query execution directly on cloud storage using Delta Lake. Because the data remains in open formats, it prevents the silos created by proprietary warehouse ingestion processes. Unity Catalog manages access at the row and column level, which allows BI analysts and data scientists to work from the same source of truth while maintaining compliance.

When to use it

  • When your organization needs to remove data duplication and synchronization overhead between warehouses and data lakes.
  • When you require a single governance model that applies to SQL analytics, machine learning, and AI agent workloads.
  • When your BI team needs to perform ad-hoc analysis on fresh, raw, or transformed data that data scientists use for model training.
  • When you want to minimize infrastructure management by using serverless compute resources for SQL tasks.

When not to use it

  • If your organization mandates a specific, non-open proprietary storage format that does not support the lakehouse architecture.
  • If your team requires highly specialized features found only in legacy operational databases that are not intended for analytical workloads.
  • Databricks SQL: Serverless compute for BI and SQL analytics.
  • Unity Catalog: Centralized governance for data, models, and access control.
  • Delta Lake: The underlying storage layer for open data formats.
  • Building GenAI applications using the same data accessed by BI teams.
  • Implementing real-time dashboards on streaming data.
  • Managing machine learning features and analytical metrics in one location.