Fast-track AI-driven Insights with Zero-Copy Data Access between SAP BDC and Google BigQuery

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SAP launched SAP Datasphere with its business data fabric architecture and open data ecosystem in 2023, with Google as one of its first partners. Since then, we’ve continued helping customers drive innovation with a data and analytics strategy that unifies SAP data and analytics with the strengths of Google Cloud’s BigQuery platform.  

Building on this partnership, we recently announced SAP Business Data Cloud (BDC) Connect for Google BigQuery at SAP Connect 2025, further strengthening our collaboration and enabling secure, zero-copy data access to curated SAP data products directly from BigQuery. SAP BDC Connect for Google BigQuery is planned to be generally available by the end of H1 2026. 

With SAP BDC Connect for BigQuery, organizations can build a business data fabric and connect their existing Google BigQuery environments to #SAP Business Data Cloud—offering data and AI teams real-time access to semantically rich SAP data products without the duplication or operational burden of multiple ETL pipelines. SAP BDC Connect for BigQuery will also be bidirectional, so organizations can break silos and democratize data with an ecosystem of business-ready data products exchanged between SAP BDC and Google Cloud Cortex Framework in BigQuery. This milestone reflects a clear architectural transformation, unlocking the potential of enterprise data for analytics and AI innovation. 

 In this blog, we take a sneak-peek into the technology behind this integration and offer prescriptive guidance with reference architectures for building seamless data integration between SAP Business Data Cloud and Google Cloud’s data and AI solutions. 

Fig 1 : SAP BDC – GBQ Integration reference architecture.  

SAP BDC Connect for BigQuery will enable seamless, secure data sharing between SAP BDC and Google BigQuery—simplifying customer landscapes and accelerating AI-driven insights and innovation.  

Third-party data, such as Google Cloud’s Weather, Trends, Geo, and Ads, can then be effortlessly integrated and harmonized with valuable SAP data like finance and supply chain. Leveraging open data protocols and advanced data fabric architectures, this approach will simplify data sharing and unify access to information across multiple systems in a heterogenous landscape. The result will be a centrally governed, holistic data environment that supports efficient analytics and AI/ML-driven use cases—all built on industry-standard open protocols.  

Challenges addressed by SAP BDC Connect for BigQuery:  

This new zero-copy integration with BigQuery aims to solve challenges caused by data duplication copies.  

• Loss of governance: Data governance becomes a major challenge once data is replicated and siloed into multiple disparate systems.  

• Loss of valuable semantics: Data extracted from business applications loses its critical semantics and business context, making it incredibly difficult to rebuild accurately and leading to issues in data quality. 

• High total cost of ownership (TCO) : Managing ETL pipelines with expensive third-party tools adds operational complexity and overhead, elevating the total cost of ownership (TCO) across the data landscape. 

• Innovation delays: Complex and fragile data pipelines hinder organizational agility, delays the delivery of actionable business insights, and restrict the pace of AI innovation and data-driven initiatives. 

Key differentiating capabilities that will be introduced by the SAP BDC Connect for BigQuery architecture include: 

• Access to AI-ready data: Accelerate AI-driven insights by combining Google Cloud's Vertex AI capabilities and analytics tools with curated, AI-ready SAP data products securely shared and governed directly from SAP Business Data Cloud.  

• Unified Data View: Deliver a harmonized view by integrating SAP data and third-party data across Google BigQuery and SAP Business Data Cloud without duplication. Build a unified, semantically rich data foundation to fuel next-gen data and AI/ML workloads using SAP Joule, Google Vertex AI, and Google Cortex framework. 

• Bi-directional data sharing: Share data products bi-directionally between SAP Business Data Cloud and Google BigQuery via their respective catalogs. This enables interoperability and re-use of these blended data sets  and derived data products . 

•  Delta Sharing protocol: Leverage bidirectional data integration between SAP BDC and BigQuery, powered by the combination of industry open standards such as Delta Sharing open data protocol and other open table formats. This enables real-time interoperability across different computing platforms, cloud environments, and applications without duplicating data. 

• Opens up flexible infrastructure choices: Leverage the highly scalable infrastructure of Google BigQuery , and powerful enterprise analytics capabilities of SAP Business Data Cloud to handle large volumes of data and complex analytics workloads. Share data products from Google BigQuery back to SAP BDC for analytics and planning in SAP Analytics Cloud and AI solutions with SAP Business AI as well.  

• Governed and secure data exchange: Provide a secure and governed experience with enhanced control over data shared via centralized access, managed by data stewardship roles. BDC Connect leverages security protocols such as mutual Transport Layer Security (mTLS) and OpenID Connect (OIDC) to ensure safe data exchanges 

• Increased scalability and data exchange speed: With the zero-copy integration,  the bottleneck of slow data transfers (as seen with data federation architecture) will be solved SAP BDC Connect that offers efficient data sharing at great scale than before.    

 The technical integration (SAP BDC Connect for Google BigQuery) enables powerful, AI-driven use cases across these business functions, detailed in the sources as strategic focuses for the partnership.

Business Function	Use Cases Enabled
Supply Chain & Operations	Predictive Inventory Optimization: Combining live SAP inventory data with marketing campaign data (e.g., TikTok, Meta, Google Ads) and external signals like weather data or Google Trends for demand forecasting.
	AI-Powered Logistics: Integrating SAP order and fulfillment data with external data (like GPS/traffic) to optimize delivery routes and predict delays.
	Enhanced Demand Forecasting: Using machine learning to analyze historical SAP sales data combined with market trends and promotions.
Marketing	Cross-Channel Campaign Optimization: Combining SAP customer sales history with audience data (e.g., LiveRamp) and campaign performance from Google Ads, YouTube, Meta, and TikTok to optimize spend and ROI.
	Personalized Marketing Campaigns: Using enriched SAP customer and product data to create highly personalized campaigns on platforms like Google Ads and social media.
Sales & Service	360-Degree Customer View: Consolidating customer data from SAP (billing, service history) with sales data (Salesforce) and service interactions (ServiceNow).
Manufacturing	Unified IT/OT Data for AI: Ingesting granular asset-level data from the shop floor (OT data) and sensor data and combining it with enterprise (IT) data for Manufacturing to improve productivity.
	Production Optimization: Gaining cross-enterprise intelligence by tracking marketing campaigns to inform product demand, production, and staffing.
Finance & Analytics	Build Grounded, Trustworthy AI: Ensuring that Google BigQuery AI models and agents operate on the most current and complete view of the business using real-time SAP data.
Sustainability	ESG Reporting and Optimization: Integrating sustainability data from SAP with other external data sources to create a unified view for comprehensive reporting and using AI to analyze energy consumption or carbon emissions.

Enhancing and scaling use cases with SAP BDC connect for Google BigQuery  

This expanded partnership also allows us to enhance previous SAP Datasphere and BigQuery use cases. While FedML successfully enabled machine-learning use cases on Google Cloud Platform using SAP data, further scaling was necessary to support data at larger volumes. Users could also replicate data to Google BigQuery using SAP Datasphere replication flows.  For example, analyzing historical SAP sales data alongside market trends and promotions typically required either replicating the data needed—complex, costly, and time-consuming—or using connector-based data federation, which is faster but less scalable.  

Now, SAP BDC Connect for Google BigQuery provides a simpler, secure, and scalable option with one-click, zero-copy data sharing via BDC catalog. This eliminates the need for data duplication , reduces data access delays usually seen with federated access, and ensures secure and governed data access (which was missing with service user-based connectors). 

In summary, the zero-copy architecture enabled by BDC Connect for Google BigQuery addresses business challenges with greater speed, lower cost, improved quality, enhanced security, and unprecedented scale. 

Credits

The above reference architectures are the results of teamwork and contributions from both SAP and Google. I would like to thank the following SAP team members for their contribution to this content  : Kevin Poskitt, Katryn Cheng, Mariusz Cisek, San Tran And  Sivakumar N,  Anirban Majumdar, Haridas Nair for their support and guidance. We also extend special thanks to the Google team members who helped assemble the guiding use-case list.
 
If you have any questions, please leave a comment below or contact us at paa@sap.com.