Databricks has introduced a Software-Defined Storage (SDS) ecosystem that seamlessly connects enterprise data estates wherever they reside—on-premises, private clouds, or edge environments—with its AI and analytics platform. This approach addresses the growing enterprise demand to govern and analyze data without costly or risky migration to the cloud.
- Enables querying of on-premises data through Databricks without migration
- OpenSharing protocol standardizes secure data sharing and governance
- Integrations with MinIO, Everpure, and Qumulo support hybrid data estates
Infrastructure signal
The announcement signals a major shift in enterprise data infrastructure strategy, moving beyond the previous 'migrate everything to cloud' approach. Instead, Databricks' Software-Defined Storage ecosystem recognizes the reality that critical data often must remain on-premises or in hybrid environments due to cost, regulatory, or sovereignty reasons. By supporting direct integration with existing storage estates, enterprises can preserve their infrastructure investments while gaining modern cloud-native analytics capabilities.
This new ecosystem relies on OpenSharing, an open-source protocol enabling secure and governed sharing of datasets directly from on-premises storages to Databricks compute environments. The approach eliminates traditional challenges related to data egress costs, latency, or compliance risk by keeping data in place. Notably, this supports standardized cataloging through Unity Catalog, providing a unified metadata and governance layer across hybrid and cloud datasets.
Developer impact
For development teams, the SDS ecosystem fundamentally changes workflows by allowing access to live enterprise data from Databricks serverless compute without requiring data duplication or ETL pipelines for migration. This simplifies data engineering and model training activities, enabling engineers to build on the freshest data in place while respecting data access controls and governance policies enforced through Unity Catalog.
The availability of open OpenSharing endpoints integrated into partner storage platforms means developers can leverage familiar Databricks APIs and tooling to query traditionally siloed on-premises or private cloud data. This reduces friction in deployment and accelerates AI/ML projects that previously faced roadblocks due to data lockdown or movement complexity.
What teams should watch
Infrastructure and engineering teams should evaluate how their current storage environments and software-defined storage providers align with OpenSharing compatibility and consider early adoption of the Databricks SDS ecosystem to reduce cloud cost and compliance risks associated with data migration. Attention should be given to integrating Unity Catalog governance uniformly across hybrid estates to ensure consistent policies.
Data science and analytics teams will benefit from improved data accessibility and freshness but should monitor performance implications of querying on-premises data via serverless compute and collaborate with infrastructure teams on optimizations. Security teams must validate the new data sharing connectivity aligns with organizational compliance and audit requirements, given the cross-environment data access model now possible.