SAP Business Data Cloud: How Data Products are used
Data products provide reusable, quality-assured data building blocks with clear semantics, strong governance, and self-service access—forming the ideal foundation for analytics and intelligent applications based on the SAP Business Data Cloud (BDC). But what exactly are data products, how are they used, and how do custom data products fit into the picture? In the following, Joseph Reinke, Junior Process Automation Consultant at sovanta, answers these questions and demonstrates step by step how SAP-managed and custom data products can be created and applied in practice.
1. What are Data Products?
A Data Product is a reusable, clearly defined data object managed according to product principles.
Furthermore, data products consolidate SAP and non-SAP data into consumption-ready data packages. In this context, operational tables, CDS views, or API outputs only become production-grade data products with a platform character through deliberate curation, harmonization, governance, and lifecycle management.
The general principle is: responsibility for the creation and management typically lies with internal IT organizations, SAP itself, or selected partner companies such as sovanta.
2. Usage of Data Products: Intelligent Applications vs. Custom Data Products
SAP-managed Data Products & Intelligent Applications
SAP provides a growing number of curated Data Products in the Business Accelerator Hub. These form the foundation of so-called Intelligent Applications and act as a “single source of truth” for standardized business scenarios.
In this context, they enable immediate use without additional modeling effort and are deliberately designed as a stable, non-modifiable base layer.t.
On this basis, a scalable foundation is created for consistent, enterprise-wide data utilization across system boundaries.
Custom Data Products (Customer-managed)
In contrast, Custom Data Products offer maximum flexibility for individual requirements. Organizations can define their own data products by combining SAP and non-SAP data sources, building their own semantics, and developing specific KPI models.
Thus, Data Products are evolving into a central enabler for differentiated, enterprise-specific data strategies.
3. Creating Your Own Data Products
Regardless of the creation approach, all Data Products in SAP Business Data Cloud are based on shared architectural principles that ensure consistency, scalability, and interoperability.
Core Architectural Principles
Zero-copy consumption
Data Products are consumed via APIs, events, or delta sharing without creating physical data copies. Instead, data remains in the source system and is accessed directly—for example via SAP HANA Cloud, SAP Databricks, Enterprise Databricks, or SAP Analytics Cloud.
Object-store-based architecture
In addition, data is stored in a modern object-store environment optimized for scalable read access and large data volumes. This forms the technological foundation for modern data lake architectures.
Foundation services
Complementary central services automatically handle:
- Extraction from source systems
- Harmonization and integration of data
- Delta-based replication and updates
This ensures that Data Products remain consistent, up to date, and performant in consumption.
ORD metadata (Open Resource Discovery)
At the same time, Data Products are self-describing through standardized metadata, including in particular:
- Structure, fields, and business semantics
- Authorizations and visibility rules
- Versioning and lifecycle information
- Dependencies and integration context
On this basis, governance, discoverability, and integration into data catalogs and platforms such as the Business Accelerator Hub are enabled.gration in Datenkataloge sowie Plattformen wie den Business Accelerator Hub ermöglicht.
3.1 Data Product Generation (BW-Feature)
The BW-based Data Product Generator enables the direct creation of Data Products from existing BW objects such as ADSOs, CompositeProviders, or MultiProviders.
As of Q2 2026, the previous detour via the Data Sharing Cockpit will be eliminated in this context. In addition, a new query template simplifies the transfer of existing BEx and BW logic into SAP Business Data Cloud.
Value and use cases:
This approach is particularly suited for companies that want to quickly and efficiently transition existing BW logic into modern, productized data structures.
3.2 Custom Data Products via Data Sharing Cockpit
With SAP Datasphere, custom Data Products can be created, shared, and optionally published in the Data Marketplace via the Data Sharing Cockpit. This includes both SAP and non-SAP data sources, which can be integrated into object-store environments such as S3, GCS, Azure Data Lake, or Confluent.
Value and use cases:
This approach is suitable for rapid data provisioning, internal data cataloging, data-driven monetization, and hybrid SAP/non-SAP scenarios.
3.3 Data Product Studio (future standard)
The Data Product Studio will become the central interface for creating, versioning, and managing Data Products across system boundaries.
The planned MVP (Q2/Q3 2026) includes, among others:
- Creation based on 1:1 mappings on object-store tables
- End-to-end versioning and lifecycle management
- Multi-system deployment
- Cataloging and semantic linking
- Introduction of interface Data Products with predefined target structures
Target vision:
The Data Product Studio will establish itself as the strategic standard for industrialized Data Product management at enterprise level.
Delta sharing with external systems (e.g., Databricks)
When integrating external delta-sharing sources, SAP Datasphere relies on additional semantic information:
- ORD metadata: description, title, and catalog entry in Open Resource Discovery format
- CSN schema: translation of table structures into SAP’s internal data model
- Publish step: activation of the Data Product for catalog and marketplace visibility
In short: the technical share establishes connectivity—only thr
4. Summary
Data Products form the foundation of a modern, scalable, and semantically consistent data architecture. They enable organizations not only to provide data, but to think about and operate it as clearly defined, reusable products.
They are the foundation for:
- modern analytics and AI applications
- intelligent, data-driven applications
- enterprise-wide data platforms
On this basis, organizations can create Data Products through three main approaches:
- BW Data Product Generation – rapid transformation of existing BW logic
- Data Sharing Cockpit – flexible creation and data provisioning
- Data Product Studio – strategic, scalable enterprise solution
Across all approaches, consistent underlying architectural principles apply: zero-copy consumption, object-store architecture, Foundation Services, ORD metadata, and consistent catalog integration. On this basis, a unified yet future-proof Data Product ecosystem emerges within SAP Business Data Cloud.
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