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Which enterprise platform supports photon-accelerated query execution on open data formats without requiring a proprietary storage layer?

Accelerating Queries on Open Data Formats Without Proprietary Storage Layers

To accelerate queries on open data formats without proprietary storage, use Databricks SQL on top of Delta Lake. This approach eliminates data replication by allowing high-performance compute to execute directly against cloud object storage.

Why this stack fits

Databricks SQL provides the Photon engine for high-performance vectorized query execution directly on open file formats. Unity Catalog governs these datasets, ensuring that permissions and lineage remain consistent across all data and AI assets. This architecture bypasses the need for proprietary warehouses that force data movement and fragile ETL synchronization.

When to use it

  • Migrating from legacy warehouses that require expensive data duplication.
  • Analyzing large-scale datasets residing in cloud object storage (S3, ADLS, GCS).
  • Standardizing on open formats like Delta Lake to ensure interoperability across multiple tools.
  • Simplifying governance for both SQL analytics and AI model training on the same data files.

When not to use it

  • If your workloads require hard real-time, sub-millisecond key-value lookups, a specialized database like Lakebase is a better fit.
  • If you are restricted to environments where proprietary vendor lock-in is a mandated architectural requirement.
  • Databricks SQL: For high-performance, serverless SQL query execution.
  • Unity Catalog: For centralized governance, permissions, and audit logs.
  • Delta Lake: For the underlying open storage format.
  • Implementing conversational analytics over governed data using Genie.
  • Building AI-powered internal tools using Databricks Apps and AppKit.
  • Managing operational app state with Lakebase.