Solution Brief

AIStor Table Sharing for Financial Services: On-Premises Data, Live in Databricks. Zero Copies.

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Who This is For:

Data and technology leaders at financial institutions who rely on Databricks for AI and analytics but need to extend that capability to regulated, high-volume on-premises data without compromising data sovereignty or expanding their compliance perimeter.

Key Takeaways
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AIStor Table Sharing embeds Delta Sharing natively into the storage platform, enabling Databricks to query live on-premises Iceberg and Delta tables without data movement, replication pipelines, or standalone sharing services.

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Eliminating data copies reduces the compliance surface area — no additional data to govern, audit, or secure in the cloud — keeping governance boundaries intact under PCI DSS, GLBA, SOX, GDPR, and OCC requirements.

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Query performance benchmarks show 1.7–4.1 second completion versus 46–94 seconds for traditional replication and sync, while customers report infrastructure reductions of 84%+ and operational headcount reductions of 87%+.

Financial institutions have standardized on Databricks for AI and analytics, but their most sensitive data stays on-premises, held there by PCI DSS, GLBA, SOX, GDPR, and OCC requirements. Getting that data into Databricks today means pipelines, copies, and stale results. This solution brief shows how AIStor Table Sharing eliminates that tradeoff. By embedding the open Delta Sharing protocol directly into the storage platform, AIStor enables Databricks to query live on-premises Iceberg and Delta tables through Unity Catalog, with no data movement and no standalone sharing server to manage. The brief documents the performance gap: queries completing in 1.7-4.1 seconds versus 46-94 seconds for traditional replication and sync approaches. It covers three supported use case categories (security and compliance log analytics, regulatory analytics, and enterprise risk and fraud modeling) and includes customer outcomes from NPCI, Nomura, and a global financial services provider across fraud detection, TCO reduction, and deployment timelines.

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