Businesses can now run analytics and AI tasks on their existing on-premises data using Databricks—without ever having to transfer it.
MinIO’s latest feature, AIStor Table Sharing, integrates with the Delta Sharing open protocol to let Databricks access live datasets stored in MinIO AIStor directly. This partnership eliminates the need for costly and time-consuming ETL pipelines or data duplication, offering a more efficient way to handle hybrid cloud environments.
Traditionally, connecting on-premises data to cloud-based analytics platforms required complex infrastructure: separate storage layers, replicated datasets, and additional governance layers that increased operational overhead. AIStor Table Sharing streamlines this process by embedding Databricks’ open-source protocol into the object store itself, allowing seamless, secure sharing across platforms without sacrificing performance or compliance.
What’s Changing—and What Isn’t
- No Data Movement Required: AIStor Table Sharing lets Databricks query on-premises datasets in real time, removing the need to copy data into cloud storage. This reduces both latency and costs associated with data transfer.
- Multi-Format Support: The feature supports both Delta and Apache Iceberg tables, giving organizations flexibility to adopt or transition between open lakehouse formats without altering their sharing workflows.
- Native Databricks Integration: Shared tables appear as first-class objects in Databricks workspaces, enabling GPU-accelerated AI tasks on local data without additional connectors or ingestion steps.
The integration also maintains strict control over data governance, allowing enterprises to define policies and sharing rules within the same storage system. This ensures compliance with local regulations while enabling hybrid workloads across regions.
Why This Matters for AI and Analytics
For industries like manufacturing, financial services, or energy—where operational data is often stored on-premises due to regulatory or performance constraints—this partnership provides a practical solution. Instead of selectively replicating subsets of data to the cloud, businesses can now leverage Databricks’ full analytics and AI capabilities directly on their in-place datasets.
MinIO’s architecture also supports scalability without vendor lock-in. All editions of MinIO AIStor share the same core binary, with differences limited to licensed features and support levels. This allows organizations to start small and expand capacity as needed, avoiding the need for a complete software overhaul when scaling.
The feature is now generally available, targeting enterprises that want faster insights while maintaining control over their data infrastructure.
