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19.2 BDC Architecture

1. Reference architecture​

                    BUSINESS DATA CLOUD
+-------------------------------------------------------+
| Intelligent applications / analytics / AI             |
|-------------------------------------------------------|
| Business data fabric                                  |
|   Datasphere | Analytics | BW | Data engineering | AI |
|-------------------------------------------------------|
| Data products | Harmonized models | Metadata          |
|-------------------------------------------------------|
| SAP sources | Third-party sources | External clouds  |
+-------------------------------------------------------+

SAP's current architecture describes BDC as a broader solution around a business data fabric, with Datasphere, Analytics Cloud, BW modernization, SAP Databricks, AI capabilities and SAP Snowflake as relevant components or extensions.

2. Component boundaries​

Do not collapse every workload into Datasphere.

Use the right component for:

  • semantic modeling,
  • analytics and planning,
  • BW modernization,
  • data engineering,
  • AI/ML,
  • data sharing.

3. Knowledge core​

SAP describes Datasphere as the knowledge core that unifies semantics, data products and modeling.

This means Datasphere remains central to the semantic architecture even when processing occurs elsewhere.

4. Data product layer​

A data product should be:

  • discoverable,
  • documented,
  • governed,
  • reusable,
  • versioned,
  • owned.

5. Architect exercise​

Map these workloads to platform components:

  • BW migration,
  • enterprise semantic model,
  • SAC planning,
  • PySpark data engineering,
  • third-party AI,
  • governed SAP application data.

Defend every placement.