Solution Brief

Extend Databricks to Your On-Premises Data: Native Delta Sharing. Zero-Copy. Zero Pipelines.

About This Resource
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Who This is For:

Data platform engineers and analytics architects at organizations that run Databricks in the cloud but store regulated, high-gravity, or latency-sensitive datasets on-premises and need live query access without replication.

Key Takeaways
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AIStor Table Sharing builds Delta Sharing directly into the storage platform — there is no separate sharing server to deploy or manage, and data never leaves the on-premises environment under any circumstances.

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Access is read-only, authenticated with scoped bearer tokens, and configurable to expire — full Unity Catalog governance including access controls, audit logs, and column-level permissions applies automatically.

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The solution supports Apache Iceberg and Delta tables natively in mixed-format environments with no rewrites, migrations, or forced standardization required.

Enterprises running Databricks in the cloud often compromise on the data those workloads can access, masking sensitive fields, downsampling volumes, or maintaining pipelines that deliver stale cloud copies. AIStor Table Sharing removes those workarounds by building the open Delta Sharing protocol directly into MinIO AIStor. Databricks queries on-premises Apache Iceberg and Delta tables live through Unity Catalog, with read-only access enforced at the storage layer via scoped bearer tokens. Data never moves. This solution brief walks through the implementation in four steps: store, share credentials, mount the catalog, and query directly. It covers supported use cases including hybrid AI/ML, enterprise analytics, and regulated workloads, and details governance specifics: token expiration, column-level permissions, and full Unity Catalog audit logs apply automatically. Databricks SVP Stephen Orban endorsed the integration for accelerating time-to-insight on hybrid workloads.

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