SAP
SAP data where analytics and AI need it.
We bring data from S/4HANA, SAP HANA and MDG into Databricks — via SAP Business Data Cloud or proven integration paths, with Unity Catalog as shared governance.
Typical situations
- SAP data reaches analytics late, incomplete or as Excel exports.
- Master data in the lakehouse doesn't match SAP.
- AI use cases need SAP context, such as suppliers, materials or orders.
- Several teams extract SAP data in their own ways — licensing, load and consistency are unclear.
- SAP Business Data Cloud or Datasphere is up for decision, and how it works with Databricks is open.
- Transactional data such as orders or stock is only loaded nightly, although business teams need fresher figures.
- Controlling and procurement want to combine SAP data with non-SAP sources.
- SAP authorizations should also apply in analytics and AI.
What we deliver
- Architecture: SAP Business Data Cloud, SAP Databricks, Delta Sharing or conventional extraction — depending on your landscape
- Connecting S/4HANA, SAP HANA and MDG master data to the lakehouse
- Data models and data products with SAP context (e.g., procurement, controlling)
- Shared governance with Unity Catalog
- AI use cases on SAP data, from forecasting to assistants
- Near-real-time loading of transactional data where business teams need current figures
- Reconciliation and quality checks between SAP and the lakehouse (completeness, matching)
- SAP authorizations carried over into the lakehouse in a traceable way
Technologies
- SAP Business Data Cloud
- SAP Databricks
- Azure Databricks
- Unity Catalog
- Delta Sharing
- SAP HANA
Outcome
A reliable SAP data foundation in the lakehouse — the basis for analytics and AI.