Data Fabric
Analytics
Semantic Layer

SAP Datasphere

SAP's business data fabric platform that unifies data from SAP and non-SAP sources with a semantic layer, enabling governed analytics without mandatory data replication. The strategic successor to SAP Data Warehouse Cloud.

Overview

SAP Datasphere is the evolution of SAP Data Warehouse Cloud (DWC), rebranded and significantly expanded in 2023. It implements the business data fabric architecture — a layer that connects, harmonises, and governs data from across the enterprise without requiring all data to be replicated into a central store. This is achieved through live data federation: queries can transparently reach into SAP S/4HANA, SAP BW, third-party databases, or cloud object storage in real time.

The platform is built around two complementary builders: the Data Builder (technical layer — data flows, replication tasks, ER models, SQL views, analytical datasets) and the Business Builder (business semantic layer — business entities, fact models, and consumption models that abstract technical complexity for SAC report builders).

SAP Datasphere integrates with the SAP One Domain Model (ODM) — a cross-application shared semantic model for SAP's key business concepts (Customers, Products, Suppliers, Cost Centres). This alignment ensures that Datasphere, SAC, and S/4HANA all share consistent business terminology.

Data Builder — technical modelling: flows, views, replication, ER models
Business Builder — semantic layer: entities, fact models, consumption models
Live data federation — query source systems without full replication
Datasphere Spaces — isolated workspaces with dedicated storage and compute
Data Marketplace — subscribe to pre-built data products from SAP and partners
SAP One Domain Model alignment for cross-application semantic consistency

Datasphere Spaces

A Space is the fundamental isolation unit in Datasphere — equivalent to a schema or namespace. Each space has its own storage allocation (HANA Cloud), assigned users and roles, connections to source systems, and data assets. Spaces promote data product thinking: a team owns a space and publishes well-defined data products for other spaces to consume.

Space-level storage quota
Space member management
Cross-space data sharing
Space-level audit log
Dedicated HANA schema
BTP tenant isolated
Space-level data flows
Data access control (DAC)

Federation vs. Replication

Live Federation (No Copy)
Recommended for real-time
  • Query runs live against S/4HANA CDS views or BW queries
  • No ETL pipeline, no storage cost, no replication lag
  • Data governance stays with the source system
  • Latency depends on source system performance
  • Best for: live KPIs, operational reports, Joule queries
Replication (Persist a Copy)
For historical / heavy analytics
  • Data copied to HANA Cloud inside the Space
  • Real-time replication via SLT or scheduled data flows
  • Faster query performance for aggregation-heavy SAC stories
  • Suitable for joining SAP and non-SAP data
  • Best for: historical trending, blended datasets, Data Marketplace products

Supported Source Connections

SAP S/4HANA (CDS views)
SAP BW / BW/4HANA
SAP ECC (ABAP CDS)
SAP SuccessFactors
SAP Ariba
SAP Concur
Microsoft Azure Synapse
Snowflake
Google BigQuery
Amazon Redshift
Databricks Delta Lake
OData V2/V4 generic
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