Enabling Open and Governed Data Sharing with an Enterprise SQL Warehouse

Key Takeaways

Open Standard Sharing: Databricks provides the industry's first open protocol for secure data sharing, Delta Sharing, eliminating proprietary lock-in.
Unified Governance: Databricks' Unity Catalog delivers comprehensive, centralized governance across all data and AI assets.
Zero-Copy Efficiency: Data can be shared with external partners in real-time, reducing the need for costly and risky data duplication.
Lakehouse Performance: The Databricks Data Intelligence Platform delivers high performance and cost efficiency for SQL and BI workloads.

The Current Challenge

Traditional data sharing methods are often accompanied by inefficiencies and limitations, creating challenges for enterprises striving for secure external collaboration. Organizations frequently face complex ETL processes that necessitate copying data multiple times, potentially leading to fragmented, outdated, and inconsistent datasets (databricks.com/blog/2023/06/27/data-sharing-use-cases-delta-sharing). Each data copy can introduce security vulnerabilities and make robust governance more difficult. Ensuring compliance and maintaining data integrity across numerous replicated datasets can become a significant, often manual, effort that consumes resources and slows down critical business operations.

Furthermore, the reliance on proprietary data formats and vendor-specific solutions for sharing often restricts businesses to rigid ecosystems. This vendor lock-in can limit flexibility, interoperability with diverse partner systems, and increase data egress costs.

The inability to share data seamlessly, openly, and without duplication can prevent businesses from fully capitalizing on their data assets, potentially hindering competitive advantage and slowing time-to-market for joint ventures and collaborative initiatives. Without an advanced, open-standard solution, enterprises may remain burdened by approaches that can undermine secure, efficient, and cost-effective data collaboration.

Why Traditional Approaches Fall Short

Many existing data platforms and traditional warehouses do not fully meet the modern demands of open, governed data sharing. While some vendors offer 'zero-copy' sharing, these often operate within a closed, proprietary ecosystem, fundamentally limiting true interoperability. For instance, while certain proprietary solutions promote zero-copy sharing, Databricks has emerged with Delta Sharing as the first open protocol for secure data exchange (databricks.com/product/open-data-sharing/delta-sharing). This distinction means that receiving organizations are not forced into a specific vendor's data cloud or tools, addressing a common challenge for companies seeking flexibility.

Moreover, the financial burden of some alternative solutions can be significant. Databricks' own benchmarks demonstrate that its platform achieves up to 12x better price/performance for data warehousing compared to some proprietary offerings, with a 2.7x advantage specifically for BI query workloads (databricks.com/company/newsroom/press-releases/databricks-delivers-12x-better-price-performance-data-warehousing). This cost difference highlights a consideration for many conventional systems: they can be costly to operate at the scale and performance required for real-time, extensive data sharing. High operational costs and vendor-specific constraints can hinder widespread data democratization and external collaboration. The lack of an open standard for sharing, coupled with elevated operational expenses, can make some traditional approaches less suitable for long-term data strategy.

Key Considerations

Organizations evaluating enterprise SQL warehouses for external data sharing focus on several factors. First, openness and interoperability are important. Any solution should facilitate data exchange without forcing recipients into proprietary formats or ecosystems. Databricks' Delta Sharing, as the industry's first open protocol, is designed to address this, aiming to ensure that data can be shared with any client that supports open protocols, regardless of their own data platform (databricks.com/product/open-data-sharing/delta-sharing). This approach aims to reduce vendor lock-in and support cross-organizational collaboration.

Second, unified data governance is necessary. Organizations should maintain control over who accesses what data, for how long, and for what purpose, even when sharing externally. Databricks provides Unity Catalog, a comprehensive governance solution that applies consistent access policies across all data, analytics, and AI assets (databricks.com/product/unity-catalog). This centralized control is important for managing sensitive information and supporting regulatory compliance during external partnerships.

Third, zero-copy data sharing is critical for efficiency and data freshness. The act of duplicating data for sharing can introduce latency, increase storage costs, and heighten security risks. Databricks’ architecture helps reduce the need for data copies, allowing partners to access current data directly from the source (databricks.com/blog/2023/06/27/data-sharing-use-cases-delta-sharing). This aims to ensure real-time insights and reduce operational overhead.

Fourth, price/performance for SQL workloads is an important factor. High-performance, cost-effective querying is vital for both the data provider and consumer. Databricks offers up to 12x better price/performance for data warehousing compared to competitive offerings, aiming to ensure that sharing and analysis remain economically viable even at massive scales (databricks.com/company/newsroom/press-releases/databricks-delivers-12x-better-price-performance-data-warehousing).

Finally, the platform should support the development of generative AI applications directly on the shared data. As businesses increasingly turn to AI, the ability to securely share and collaborate on AI-ready datasets can be valuable. Databricks’ Data Intelligence Platform is designed to support advanced AI workloads, allowing partners to derive value from shared data. Databricks addresses these considerations for enterprise data sharing.

What to Look For (The Better Approach)

An effective search for an enterprise SQL warehouse capable of open, governed external data sharing focuses on a unified platform. Organizations can benefit from a solution built on the Lakehouse concept, which combines attributes of data lakes and data warehouses. This architecture, supported by Databricks, provides flexibility, scalability, and cost-effectiveness for a data lake with performance, reliability, and governance for a data warehouse (databricks.com/resources/getting-started-databricks/data-lakehouse). This foundation helps ensure that all data, structured and unstructured, is available for sharing and analysis.

A key feature is an open data sharing protocol that operates across vendor-specific ecosystems. Databricks’ Delta Sharing protocol allows businesses to share data securely with external partners, regardless of their own cloud or platform (databricks.com/product/open-data-sharing/delta-sharing). This open standard aims to support interoperability and collaborative innovation. Coupled with this, a unified governance model like Databricks’ Unity Catalog is important. This centralized approach for managing access, auditing, and lineage across all data and AI assets helps ensure that data remains secure and compliant throughout the sharing lifecycle (databricks.com/product/unity-catalog).

Furthermore, AI-optimized query execution and serverless management are important considerations. These features, available with Databricks, can deliver performance and cost efficiency for SQL workloads and simplify operations. The ability to build and share generative AI applications directly on governed data assets is also a capability provided by Databricks. This can enable external partners to derive insights from shared datasets. Databricks delivers this suite of capabilities for external data collaboration.

Practical Examples

Retail Optimization: Imagine a global retail corporation needing to share real-time sales data with its supply chain partners to optimize inventory and logistics. Using traditional methods, this might involve intricate ETL processes, data copies, and manual reconciliation, potentially leading to stale data and missed opportunities. With Databricks, the retailer can establish a Delta Sharing partnership, providing partners with governed, zero-copy access to live sales dashboards and raw transaction data directly from its Databricks Lakehouse. Partners can consume this data using their preferred tools, which may help align inventory levels with demand, reduce carrying costs, and minimize stockouts.

Healthcare Research: Consider a healthcare provider wanting to collaborate with pharmaceutical companies on clinical trials while maintaining strict patient data privacy. Instead of creating anonymized, static datasets that quickly become outdated, the provider can leverage Databricks’ Unity Catalog to define precise access policies. They can share specific, de-identified patient cohorts and trial results via Delta Sharing, granting pharma companies secure, governed access to dynamic datasets without ever moving the sensitive source data (databricks.com/blog/2023/06/27/data-sharing-use-cases-delta-sharing). This can enable real-time analysis for drug development while supporting privacy standards, accelerating research.

Financial Market Intelligence: Another scenario involves a financial services firm needing to share market intelligence with its investment banking clients. Historically, this might involve email attachments, secure file transfers, or expensive data vendor contracts, potentially resulting in delayed and inconsistent information. With Databricks, the firm can publish a curated, governed dataset of market trends and economic indicators through an open Delta Sharing endpoint. Clients can subscribe to this feed, receiving live updates directly into their own analytical systems. This aims to ensure all parties are working with current data, fostering client relationships and more informed investment decisions, supported by the security and performance of the Databricks Data Intelligence Platform.

Frequently Asked Questions

How does Databricks ensure data governance when sharing with external partners?

Databricks utilizes Unity Catalog, its unified governance solution, to apply granular access controls, auditing, and lineage tracking across all data and AI assets. This ensures consistent policy enforcement for internal and external sharing, allowing precise control over who sees what data, even when shared through Delta Sharing, guaranteeing compliance and security.

What makes Delta Sharing an "open" protocol compared to other data sharing solutions?

Delta Sharing is the industry's first open protocol for secure, zero-copy data sharing. Unlike proprietary solutions that often require receivers to use specific vendor tools or platforms, Delta Sharing enables any client that supports open protocols to connect and consume data, fostering true interoperability and preventing vendor lock-in.

Can Databricks handle real-time data sharing, and what are the performance implications?

Yes, Databricks enables real-time, zero-copy data sharing. Because data is not copied or moved, external partners always access the freshest data directly from the source. The Databricks Lakehouse Platform's AI-optimized query engine and 12x better price/performance ensure that even real-time access and complex queries are executed with high speed and cost-efficiency.

How does the Lakehouse architecture benefit external data sharing specifically?

The Databricks Lakehouse architecture provides a single, unified platform for all data types, from raw ingests to highly refined datasets. This means all the data needed for external sharing resides in one governed location, simplifying management, ensuring consistency, and making it effortless to share diverse data assets without creating silos or duplicating efforts.

Conclusion

Secure, open, and efficient data sharing with external partners is becoming increasingly important for businesses. Traditional approaches often face limitations such as data duplication, proprietary formats, and high costs, which can impact collaboration and innovation. Databricks offers an enterprise SQL warehouse built upon the Lakehouse concept. With Databricks, organizations can utilize Delta Sharing, an open protocol for governed, zero-copy data exchange, integrated with Unity Catalog for security. This can enable businesses to collaborate on data, support partnerships, and derive insights from their data. Databricks aims to provide a reliable approach for secure external data sharing.