Announcing Odaseva Zero-Copy Data Sharing for Snowflake: Grounding Salesforce Customer Context in the Snowflake AI Cloud

We’re excited to announce that Odaseva Zero-Copy Data Sharing for Snowflake is now live. 

With this managed service, companies can project live Salesforce data or historical archives directly into Snowflake^® via Apache Iceberg. It repurposes complex CRM data into near-real-time and shareable data products, overcoming API and ETL bottlenecks through a secure zero-copy bridge with no physical data movement or custom code required. 

For enterprises, the business impact is immediate: slashing the total cost of ownership (TCO) of data integration, reclaiming thousands of data engineering hours, and accelerating time-to-value for AI initiatives.

Let's explore why this architectural shift is critical for the era of Agentic AI, how open data standards and Snowflake’s Interoperable Lakehouse have enabled it, and how Odaseva can transform your Snowflake environment into an open customer data lake with complete Salesforce context in just a matter of days.

The Challenge: The AI Data Bottleneck and The Rise of Open Data Architectures

The enterprise data landscape is at a critical inflection point. With global AI spending projected to reach $2.59 Trillion according to Gartner, organizations are discovering that the primary bottleneck to AI success isn't the LLMs, but the enterprise data context fueling them. 

Historically, organizations relied on a "copy-first" mindset using brittle ETL/ELT pipelines to duplicate operational data. However, physical data movement creates latency, inflates storage costs, expands the security blast radius, and fractures governance, ultimately leading to AI hallucinations and broken workflows. Furthermore, surveys indicate that data engineering teams still spend half of their time maintaining these fragile pipelines rather than driving business value.

This is driving a paradigm shift toward open data architectures, now that modern serverless architectures definitively decouple compute from storage. Today, data no longer needs to be physically copied into proprietary query engines, but can rather be standardized and seamlessly shared at its source using open standards like Apache Iceberg and Apache Polaris.  

This data independence dismantles vendor lock-in and ensures organizations retain true sovereignty over their data. The shift is already here: Ryft’s 2026 State of Apache Iceberg report shows that 95% of organizations are using or planning to use Iceberg for AI workloads, and 79% plan to move their remaining data to Iceberg within the next 12 months.

Recognizing this shift, Snowflake introduced the Snowflake Interoperable Lakehouse. At the core of this foundation are Apache Iceberg and Apache Polaris. Together, they guarantee bidirectional read/write interoperability, allowing multiple data engines to query the exact same data without physical duplication or vendor lock-in. 

This open physical layer is enhanced by unified governance and semantics. Through Snowflake Secure Data Sharing and the Horizon Catalog, organizations grant live, in-place access where security and masking policies persist universally across all engines. Furthermore, Snowflake’s investment in Apache Ossie provides a portable semantic layer, ensuring AI agents and BI tools share a single, governed understanding of business context.

Zero-Copy Data Architecture: Building Federated Data Products

Historically, enterprise architecture enforced a strict boundary between systems of record (such as CRM or ERP platforms) and systems of insight (such as data warehouses and BI tools). Today, that boundary is dissolving: operational systems must embed real-time intelligence, while systems of insight must become directly actionable through Agentic AI.

This convergence requires abandoning the legacy ETL-driven "Move Data to Applications" model, which burdens engineers with fragile pipelines, ingestion bottlenecks, and failover vulnerabilities. Instead, forward-thinking enterprises are adopting a "Bring AI to Data" pattern by building federated data products. 

In a customer data lake, this requires decentralization: domain experts (e.g., the Salesforce CRM team) can package and securely share their data into an always-on data product stored in open formats. The enterprise data platform ingests only logical pointers and metadata. The CRM team can then guarantee strict data contracts (e.g. on data latency, data availability, privacy by design), allowing data consumers to simply plug into the data product without navigating complex, restricted and constantly changing source system APIs.

This federated architecture unlocks three transformative benefits:

• Frictionless data sharing: AI agents rely on a complete, always-up-to-date data landing zone without pipeline delays.
• Elimination of the "ETL Tax": Organizations drastically reduce integration compute fees, storage duplication, and engineering maintenance hours.
• Unified Governance: Security and masking policies are defined once at the source and propagated downstream via catalogs.

However, as enterprises architect these zero-copy data products, a word of caution is necessary regarding today's SaaS landscape: Megavendors (such as CRM and ERP giants) recognize that source data is their ultimate competitive moat and that context is the cornerstone to enterprise AI. 

While supporting bidirectional zero-copy architectures, they tend to establish commercial models favoring inbound over outbound integration: inbound data flows are free, while querying data externally in engines like Snowflake is gated by unpredictable pay-per-use fees. To succeed, a true zero-copy strategy must utilize decoupled, vendor-agnostic infrastructure to control independence and the total cost of ownership. 

Unlocking Open Customer Data Lakes: Odaseva Zero-Copy for Snowflake

Salesforce CRM data remains one of the most critical, yet historically difficult, assets to liberate for enterprise AI. Odaseva Zero-Copy Data Integration for Snowflake eliminates the friction, complexity, and performance risks of traditional CRM data integration while ensuring organizations retain complete data independence. 

By leveraging Snowflake Secure Data Sharing, Apache Iceberg tables, and the Snowflake Horizon Catalog, Odaseva acts as a secure bridge that bypasses fragile ETL pipelines entirely. The managed service extracts and normalizes Salesforce data into open Apache Iceberg tables hosted in a dedicated environment optimized for Snowflake. This data is securely shared directly with the customer’s Snowflake account without physical data movement, replication, or egress overhead. 

By transforming live CRM data, complete backup histories, and deep archives into governed Open Customer Data Lakes, Odaseva delivers sharp, quantifiable enterprise outcomes: 

• Data Engineering Excellence & Scale: The autonomous, self-healing service automates schema evolution and overcomes Salesforce API limits, allowing data teams to shift their focus from fixing broken pipelines to driving AI innovation. Surveys estimate that managed data integration approaches help reclaims 15-20% of engineering hours previously lost to pipeline maintenance, schema drift, and API throttles, while shortening recovery times to around 11 hours. 
• Lower Costs and FinOps Efficiency:  By avoiding physical data duplication and heavy integration compute fees, Odaseva protects enterprises from unpredictable row-based or pay-per-use query taxes, delivering predictable flat-fee pricing. According to industry survey benchmarks, potential enterprise savings include a 50%–80% reduction in storage spend, 20%–35% lower compute costs. 
• Instant AI Context for Data Scientists: Data teams gain instant, direct access to complete customer context, including deep archives, attachments, calculated fields, hard deletes or field histories, without filing IT tickets or exhausting API quotas. This accelerates readiness for platforms like Snowflake Cortex, grounding AI models with rich operational context without compromising compliance. 
• Data Independence & Security: Operating as an independent data source provider, Odaseva enforces strict governance with granular end-to-end encryption and Customer Managed Keys (CMK), ensuring the enterprise completely controls its data. Furthermore, Odaseva provides robust, controlled failover capabilities to prevent uncontrolled outages or API timeouts, ensuring the CRM context powering mission-critical Agentic AI workflows remains highly available and resilient. 

Learn more about: 

• Snowflake Interoperable Lakehouse
• Odaseva Zero-Copy Data Integration for Snowflake 

And request a demo today.

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