Sep 10, 2026
10.9.2026
10.9.2026
SAP and Snowflake have been partners for some time. Now, SAP Business Data Cloud (BDC) and Snowflake are becoming even more tightly integrated. What many have long been waiting for is now possible: data sharing— including SAP semantics—as a foundation for analytics and AI. We explain the key fundamentals, show how hyperscalers fit into the picture, outline the available models, and walk through the technical implementation. Above all, we explore the opportunities this integration creates.
This article provides an overview of how SAP BDC and Snowflake work together and explains the differences between the two options: SAP Snowflake and SAP BDC Connect for Snowflake. We also cover the fundamentals of zero copy, data products, and bidirectional data sharing, as well as the role of hyperscalers. Finally, we present typical use cases to give you a concise guide to choosing the model that best fits your data strategy.
Anyone who has worked with data platforms and data management for some time will be familiar with the challenge of SAP data integration. Until now, there have been several ways to approach it—but each involved trade-offs: complex ETL pipelines, lost semantics and relationships, unclear naming conventions, and difficult-to-manage bidirectional data flows. In short, it has been time-consuming, complex, and resource intensive.
Since May 2026, there has been a new approach—and it is more than just another ETL/ELT connector: a bidirectional, semantics-preserving zero-copy model. The goal is to make SAP data available in Snowflake without extracting it in the traditional way and losing context, governance, or data freshness in the process.
But let’s take it one step at a time: What is SAP BDC, and how is it connected to Snowflake?
SAP BDC stands for SAP Business Data Cloud, SAP’s data platform and business data fabric approach. Its central building blocks are SAP Data Products: curated data from existing SAP systems, such as S/4HANA and SuccessFactors, including semantics, metadata, and business context. Examples of such data products include revenue and product category data.
SAP deliberately positions BDC as an open platform that can connect to multiple platforms, including Snowflake, Databricks, BigQuery, Microsoft Fabric, and AWS services. For Snowflake, there are two options:
Both options focus exclusively on sharing SAP Data Products using a zero-copy approach. In principle, the two models can also be combined if doing so makes technical and economic sense.
The partnership’s most significant innovation is zero copy. It differs most notably from previous architectural approaches to using SAP data on data platforms.
Until now, traditional SAP data integration with Snowflake—or comparable data platforms—typically followed this process: SAP data was extracted from SAP systems using connectors, third-party software, or ETL frameworks; it then went through an ETL/ELT process, was stored, potentially prepared, and only then made available for use in Snowflake. This inevitably created data copies—at least two: one in SAP and one in Snowflake. That comes with several disadvantages:
Zero copy changes this principle. An SAP Data Product can be made available directly in Snowflake through sharing—without ongoing pipelines, duplicated data, or additional maintenance effort. It can be understood as data sharing without moving the data. This also makes it easier to track changes to the data more quickly.
Important: Zero copy does not mean that no integration is required. Permissions, shares, connections, semantics, governance, and lifecycle management still need to be properly maintained. However, the major benefit remains: no data movement means no synchronization issues.
This effectively answers the question, “How does data get from SAP to Snowflake?” Instead, a new question becomes relevant: How can SAP data be correctly made available in Snowflake as a Data Product? This is where the fundamental difference from traditional architecture lies.
Technically, Snowflake integrates SAP Data Products as “catalog-linked databases.” Semantic views can also be generated from SAP Core Schema Notation. These can be used, for example, for:
Unlike in the past, this approach does not transfer only technically relevant fields. It also includes metadata, semantics, relationships, and governance. This not only simplifies monitoring and automation but also provides an important foundation for using AI. Put simply, AI is not merely presented with an SAP column. It understands what “revenue,” “company code,” or the relationship between them means—and how it should be interpreted in a business context.
SAP summarizes it as follows: “Business context is preserved across platform boundaries.”
This works in the other direction as well: from Snowflake back to SAP BDC. SAP data can therefore be enriched and enhanced, opening valuable new perspectives for both AI applications and day-to-day analytics. For example, organizations can incorporate weather, market, or other data from their own sources, as well as third-party data from the Snowflake Marketplace. In technical terms, this is known as an enrich-and-return workflow, which can significantly enhance both an organization’s data and the insights derived from it.
Snowflake depends on hyperscalers, which is why they also play an important role in the SAP BDC and Snowflake ecosystem. Snowflake runs natively on AWS, Microsoft Azure, and Google Cloud. The same applies to SAP Business Data Cloud. Both providers clearly position themselves as multi-cloud data platforms—or, in SAP’s case, as multi-cloud compatible.
One key difference is that the SAP BDC connection to Snowflake is currently available for AWS and Azure. Google Cloud support is planned and expected to follow by the end of 2026.
Now that we have covered the basics, functionality, and availability, let’s get practical: What opportunities does this create in concrete terms?
The simple answer is whenever SAP and non-SAP data come together. Here are two examples:
In short, whenever internal or external sources and Marketplace data need to be combined with SAP data, the SAP BDC approach is worth considering—regardless of the option selected. It enables a new way of thinking about data usage: no longer “SAP only,” but instead using data where it delivers the greatest value.
That does not mean traditional ETL/ELT pipelines will disappear entirely—quite the opposite. If data must be permanently extracted, transformed, historized, materialized, or archived, established approaches will remain necessary. In these cases, BDC simply complement the existing architecture with a sharing and federation model.
Want to make SAP data available in Snowflake without the effort of creating and maintaining copies? Whether through SAP Snowflake as an integrated solution or BDC Connect for your existing Snowflake environment, we support you end to end—from architecture through production deployment.
Domain Lead Data Platform & Data Management
Domain Lead Data Platform & Data Management
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