AIStor Table Sharing connects Databricks directly to Delta and Iceberg tables stored on-premises, with no replication, pipelines, or cloud migration required. Data stays under your governance model while Databricks queries it live using the open Delta Sharing protocol. Keep storage where it performs the best and costs the least least, without compromising the analytics experience your teams depend on.
Databricks queries live Delta and Iceberg tables in place, without replication or staging.
Zero-Copy Hybrid Architecture
No data movement. No mirrored cloud storage. No synchronization pipelines to build and maintain.
Open, Standards-Based Sharing
Native Delta Sharing 1.0 implementation. No proprietary gateways. No additional sharing servers.
Unified Governance at the Source
Define, secure, and publish shares from the same system where the data resides. Enforce read-only access at the storage layer.
Lower Cloud Storage Costs
Avoid duplicating petabytes of data just to make it analyzable.
How It Works
AIStor embeds Delta Sharing directly into object storage, eliminating the infrastructure and operational overhead typically required to expose on-premises data to cloud analytics platforms.
Native Storage-Level Integration
Delta Sharing is built into AIStor itself.
No standalone Delta Sharing server
No reverse proxies or sidecar services
No separate governance plane to manage
Live, Zero-Copy Table Access
Databricks connects using open protocol support and queries tables in place.
Data remains on-premises
Only query results move to compute
No version drift or stale cloud replicas
Unified Table and Share Lifecycle
Storage, catalog, and sharing operate as one system.
Shares update automatically as tables evolve
Governance enforced where the data physically lives
One source of truth across hybrid environments
Enterprise-Scale Foundation
Built on AIStor’s high-performance, strictly S3-compatible object storage.
Linear scaling from terabytes to exabytes
No centralized metadata bottlenecks
Designed for on-premises and hybrid architectures
Data remains on-premises
Proven Results
Faster Time-to-Insight
Eliminate transfer delays and stale snapshots. Work on live data.
Reduced Total Cost of Ownership
Avoid duplicating large datasets in the cloud. Reduce infrastructure sprawl and operational overhead.
Stronger Governance and Risk Control
Enforce access at the storage layer. Keep regulated and sensitive data on-premises.
Simplified Hybrid Architecture
Remove standalone sharing servers, custom pipelines, and synchronization logic.
Built for Real-World Applications
Financial Services
Fraud detection on live transaction data
AML & regulatory compliance analytics
Credit risk modeling on regulated datasets
Telecom
Network telemetry & performance analytics
Billing fraud & revenue assurance
Subscriber churn & experience modeling
Life Sciences
Clinical, lab, and image analysis
Drug discovery AI pipelines
Research data governance across hybrid environments
Manufacturing
AI inference for production and quality analytics
Predictive maintenance on OT/historian data
Digital twin & simulation modeling
Media
Content rights & licensing analytics
Audience behavior & engagement modeling
Ad revenue reconciliation
Gaming
Player behavior & telemetry analytics
Virtual currency & fraud detection
Server performance & capacity modeling
Connect Databricks to the Data That Drives Your Business
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