Analyst(s): Brad Shimmin
Publication Date: November 6, 2025
The November 4, 2025, partnership announcement between SAP and Snowflake is a market-defining move that directly targets the biggest bottleneck in enterprise AI: getting trusted, context-rich business data to the models that need it. By enabling bi-directional, zero-copy data sharing between SAP Business Data Cloud (BDC) and Snowflake’s AI Data Cloud, this collaboration offers a powerful alternative to the brittle and slow ETL pipelines that have defined enterprise data integration for decades. This architectural shift further validates the data fabric concept, where data is accessed securely in place rather than moved. In so doing, it reframes traditional ETL as a legacy approach for modern AI workloads. For enterprise leaders, this partnership is a significant win that shifts the focus from choosing a single vendor to architecting a flexible, multi-platform data strategy for the AI era. However, its ultimate success will hinge on robust governance frameworks and the strategic upskilling of data teams.
What is Covered in this Article:
- The strategic SAP Snowflake partnership hopes to re-architect the way enterprise practitioners think about bringing data to AI workloads.
- The introduction of zero-copy, bi-directional data sharing between SAP Business Data Cloud and Snowflake’s AI Data Cloud.
- Establishment of a unified data fabric, strengthening the ongoing shift away from traditional ETL complexities, particularly for real-time AI needs.
- New offerings include the SAP Snowflake Solution Extension for SAP BDC and SAP Business Data Cloud Connect for Snowflake.
- The partnership exemplifies the idea of coopetition as a market standard, no doubt driven by customer demand for open ecosystems and best-of-breed solutions.
The News: On November 4, 2025, SAP and Snowflake announced a deep technical partnership aimed at erasing the boundary between SAP’s core business data and Snowflake’s cloud-native platform. This isn’t just about API connectors; it’s a foundational redesign of data access. The core of the announcement is bi-directional, zero-copy integration. In practical terms, this means that data in SAP can be queried and accessed live from Snowflake (and vice versa) without being physically copied or moved. This is enabled through federated access technologies that allow one platform to treat data in the other as if it were native. This drastically reduces latency, eliminates data duplication costs, and ensures decisions are made on live, consistent information.
SAP and Snowflake Redefine Enterprise Data for AI: Is Your ETL Strategy Already Obsolete?
Analyst Take: The most sophisticated Large Language Models (LLMs) are practically useless for enterprise functions if they can’t access and understand real-world business context. This is a widespread “grounding” problem that leads to AI model hallucinations, inaccuracies, and inconsistencies.
The partnership between SAP and Snowflake directly addresses this issue by creating a high-bandwidth, secure pipeline from the semantic layer of business operations (SAP’s domain) to the AI development and execution layer, where Snowflake excels. Preserving SAP’s business context allows enterprises to feed their AI models with data that’s not just accurate but also meaningful. This is how you turn a generic chatbot into a supply chain co-pilot that understands your inventory levels in real-time.
For years, vendors have pushed the idea of a single, monolithic platform as the enterprise’s single source of truth. This partnership validates a more pragmatic and powerful reality, namely the federated nervous system. The goal is no longer to consolidate all data in one place, but to create a unified fabric for secure, governed access across best-of-breed platforms. This is a fundamental shift from a monolithic central brain to a distributed intelligence network.
ETL’s Role Has Been Redefined, Not Eliminated
Is ETL dead? No, but its primacy is over. For AI-driven use cases requiring real-time data (e.g., fraud detection, dynamic pricing, predictive maintenance), traditional batch ETL is a non-starter. This zero-copy model represents the new standard for live data integration. Legacy ETL will still have a role for archival, large-scale batch transformations, and data warehousing where latency isn’t the primary concern. However, for any organization serious about operationalizing AI, building another brittle, nightly batch job to extract SAP data is now a clear architectural anti-pattern. The cost, complexity, and data consistency issues are simply no longer justifiable.
This Isn’t a Monogamous Relationship
While the SAP-Snowflake announcement is significant, it’s a mistake to view it in a vacuum. This is a deliberate and strategic play by SAP to position SAP Business Data Cloud as an open, central hub in a multi-cloud world. SAP already has similar integrations with Databricks and Google. This sort of coopetition is the new market reality. SAP wins by making its data accessible to the leading cloud data platforms. Snowflake wins by gaining sanctioned, high-performance access to the world’s most valuable business data. And system integrators serving SAP and its zero-copy partners (including Deloitte, Capgemini, and Accenture) will see a significant uptick from all angles. Ultimately, though, customers are the biggest winners here, gaining the flexibility to use best-of-breed tools without being punished by vendor lock-in.
The key to success for both SAP and Snowflake will rest in how quickly and effectively they can bring these new offerings to market. This isn’t a simple API connector they’re shipping next month. This is deep, foundational plumbing between two of the most complex data platforms on the planet. The 2026 timeline is a signal of this engineering reality and will demand some work from both vendors and their customers. Unifying governance, security models, and metadata across SAP’s sophisticated semantic layer and Snowflake’s cloud-native architecture is a monumental task. This isn’t just about federating queries; it’s also about federating data. It’s about federating trust, and getting that right for mission-critical enterprise data will take some time.
What to Watch:
- Data Products Now: Enterprise data practitioners shouldn’t wait to invest in building data products as though they were software. Start cleaning up SAP roles and authorizations now. Identify which data assets are prime candidates for exposure to Snowflake and establish the corresponding access policies. If the governance model isn’t in order, this powerful integration will only amplify that chaos.
- The Governance Gauntlet: Both vendors must deliver a truly unified governance framework that simplifies security and ensures compliance. The success of this partnership hinges on their ability to prove this model is not only robust but also secure and manageable for highly regulated industries.
- The Scramble for Skills: The shift from ETL pipelines to data fabric management will create a market-wide demand for a new kind of data professional —one who is part data architect, part governance expert, and well-versed in both the development and use of AI solutions. Enterprises will need to rapidly upskill their teams to capitalize on this technology, creating a near-term talent bottleneck.
- Acceleration of ETL’s Decline: The success of this high-profile partnership will accelerate the enterprise shift away from traditional ETL for modern analytics and AI. This will inspire other platform vendors to prioritize zero-copy integrations, solidifying the data fabric as the default architectural pattern for the AI era and forcing a strategic re-evaluation of data integration spending across the market.
See the complete press release on the collaboration between these two companies on Snowflake’s website.
Disclosure: Futurum is a research and advisory firm that engages or has engaged in research, analysis, and advisory services with many technology companies, including those mentioned in this article. The author does not hold any equity positions with any company mentioned in this article.
Analysis and opinions expressed herein are specific to the analyst individually and data and other information that might have been provided for validation, not those of Futurum as a whole.
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Author Information
Brad Shimmin is Vice President and Practice Lead, Data Intelligence, Analytics, & Infrastructure at Futurum. He provides strategic direction and market analysis to help organizations maximize their investments in data and analytics. Currently, Brad is focused on helping companies establish an AI-first data strategy.
With over 30 years of experience in enterprise IT and emerging technologies, Brad is a distinguished thought leader specializing in data, analytics, artificial intelligence, and enterprise software development. Consulting with Fortune 100 vendors, Brad specializes in industry thought leadership, worldwide market analysis, client development, and strategic advisory services.
Brad earned his Bachelor of Arts from Utah State University, where he graduated Magna Cum Laude. Brad lives in Longmeadow, MA, with his beautiful wife and far too many LEGO sets.
