SAP Databricks vs. Native Databricks: Choosing the Right Platform
- Big Data Analytics, Databricks, SAP BDC Databricks, SAP Business Data Cloud (BDC)
- Databricks
- 5 min reading time
Dr. Andreas Wagner
In the world of data analysis, companies often face a decision that feels like the classic engineering question asked before every rocket launch: What requirements must the rocket meet to be suitable for the mission? In other words: Should we choose SAP Databricks or Native Databricks? This article highlights the key differences and similarities between the two solutions.
In the past, integrating SAP data into Databricks was a laborious process involving flat file exports, rigid ABAP extractors, and the manual replication of complex SAP logic. Today, integration with the SAP Business Data Cloud (BDC) breaks down these silos. The purpose of this article is to provide you with a solid basis for deciding which platform best fits your specific IT strategy.
Table of contents
1. Differences Between SAP Databricks and Native Databricks
Although both solutions are based on the same core technology—meaning our two rockets essentially come from the same factory—they are designed for different mission profiles:
- Native Databricks: The open data platform licensed directly from the vendor. It offers maximum flexibility for all types of data sources and cloud infrastructures (Azure, AWS, GCP).
- SAP Databricks: A specialized OEM solution licensed exclusively through SAP. It is deeply integrated with SAP Datasphere and optimized to make valuable business semantics—such as hierarchies, currency conversions, and CDS views—directly accessible.
2. direct comparison: SAP Databricks vs. native Databricks
| Characteristic | SAP Databricks (OEM) | Native Databricks |
|---|---|---|
| Main purpose | Specializing in SAP data; a catalyst for innovation in SAP data management. | A universal platform for enterprise-wide data engineering and data science. |
| Integration focus | Seamless integration via the BDC connector; focus on preserving SAP semantics (no need to recreate the logic). | Open to hundreds of third-party sources and cloud services; BDC Connect for SAP. |
| ML Development | Closes the gap to the Pro-Code ML Workbench within SAP BDC. | Full MLflow integration and freedom to choose your tools. |
| Licensing | Part of the SAP subscription; consolidated billing via SAP. | Directly through Databricks; cloud infrastructure is managed separately. |
| Technology stack | Based on Databricks, supplemented with SAP Blueprints and a reduced feature set compared to native Databricks (dashboards, data apps). | A pure Databricks stack (Spark, Delta Lake, MLflow) for maximum technical freedom. |
3. The Key Aspects in Detail
The Integration Revolution: Moving Away from "Plumbing"
In the past, teams spent 80% of their time laboriously reconstructing SAP data structures in Databricks—a process known as “data plumbing.” A rocket launch? Not a chance!
With the new SAP BDC Connector for Databricks, hierarchies and business logic are “understood” directly and streamed in real time. The shift from mere data transport to direct business value feels like the moment when ground crew can finally give the go-ahead—because the fuel system is working seamlessly for the first time.
The Missing Piece of the Puzzle: Pro-Code ML Workbench
A key technical advantage: SAP BDC alone does not provide a comprehensive pro-code ML workbench for data scientists without SAP Databricks. Only through the integration of SAP Databricks do developers gain access to a full-fledged environment for Python, Scala, and R—directly on their SAP data products. This transforms BDC from a pure data management platform into a true AI factory.
Synergies within the BDC
It is important to understand that while the SAP Databricks OEM version does not include some “native-only” features, this is strategically offset. Missing functions in the Databricks core are handled by other SAP BDC components—such as the Datasphere semantic layer or BTP services. The result is a harmonized overall system rather than a collection of individual solutions. Each component plays its part in the mission.
Licensing Model & Governance
SAP Databricks offers the advantage of integrated governance. Identities and security policies from the SAP ecosystem are more easily harmonized thanks to the tight integration with the Unity Catalog. In addition, customers benefit from consolidated billing within their existing SAP contracts.
4. Modern Data Platform: The Hybrid Gold Standard
To meet the needs of modern businesses, a data platform today must do more than just store data. And sometimes it’s not a single rocket that makes a mission possible—but the interaction of multiple stages.
A hybrid approach—combining SAP BDC with SAP Databricks and Native Databricks—meets the four key requirements:
- Top-notch usability: Business users work in the familiar SAP environment (SAC), while data scientists use the familiar Databricks environment for Python/SQL (SAP Databricks and native Databricks). Both access the same semantically rich data.
- Unified Architecture: Through delta sharing and zero-copy connectivity, SAP and non-SAP data are consolidated into a unified lakehouse model without creating costly data silos. The most effective way to determine which Databricks to use is through the principle of data gravity: a use case is implemented where the majority of the data resides.
- Support for all workloads: From traditional BI/reporting (SAC) to real-time streaming (LakeFlow) and advanced AI (Mosaic AI)—the hybrid stack covers the entire spectrum.
- Universal Governance: Security and control do not end at the boundaries of the SAP world.
The Foundation: How Governance Becomes Universal
A seamless identity and authorization pipeline is the unsung backbone of every secure data platform. It works best when no one even notices it’s there—like a perfectly coordinated team in a mission control center:
- Microsoft Entra ID (Identity Provider): The single source of truth for all user identities.
- Identity Provisioning System (IPS): Automatically synchronizes identities between Azure and SAP cloud services.
- SAP Cloud Identity Service (IAS): Ensures that users are uniquely identified in SAP applications such as Datasphere.
- Unity Catalog (Databricks): Uses the same identities via Entra ID to enforce granular access controls at the table, row, and column levels.
The result:
When a user is deactivated in Entra ID or changes roles, their access is immediately and consistently adjusted across the entire hybrid stack—from SAP Datasphere to Databricks Notebook. Only this integration guarantees true universal governance.
5. Conclusion: Which rocket is right for your mission?
The decision is not a matter of which technology is better, but of strategic priority:
- Choose SAP Databricks if you’ve already made significant investments in the SAP ecosystem. It drastically reduces complexity, provides the necessary pro-code capabilities for ML, and enables you to feed SAP data directly into AI models—without losing any business logic along the way.
- Choose Native Databricks if you prefer a cloud-agnostic architecture and need maximum flexibility across a highly heterogeneous data landscape.
- Choose the hybrid approach if you don’t want to compromise on usability and governance and are looking for a future-proof platform that combines SAP depth with enterprise breadth. It’s essentially the best of both worlds—all in one solution.
Ready to compare solutions in your own IT environment? Let’s discuss, with no obligation, which approach is right for you.
Andreas & Yvonne's Databricks-Guide
Would you like all the important information at a glance?
Download the free guide to SAP Databricks now!
Your data strategy is individual - your consulting should be too
The choice between SAP Databricks and native Databricks depends on countless factors: Your existing system landscape, your business goals and your data culture. There is no standard answer.
Let's talk without obligation about which route is right for you. Contact us for a personal consultation.
