SAP Databricks vs. Native Databricks: The detailed comparison for your company
- Big Data Analytics, Databricks, SAP BDC Databricks, SAP Business Data Cloud (BDC)
- 5 min reading time
Dr. Yvonne Avaro
In the world of data analysis, companies are often faced with choosing the right platform to make the best use of their data. This article highlights the key differences and similarities between two leading solutions: SAP Databricks and native Databricks. We explain which use cases and business strategies each platform is suitable for and what the key differentiators are in terms of integration, licensing and technology. The aim is to provide you with a sound basis for deciding which of these powerful platforms is best suited to your specific requirements.
Table of contents
1. differences between SAP Databricks and native Databricks
When it comes to advanced data analytics and artificial intelligence, both SAP Databricks and native Databricks are powerful tools. But which solution is right for your business? The answer lies in the details, because although both are based on the same core technology, they are designed for different use cases and target groups.
Native Databricks is the open, generic data platform that is licensed directly from the manufacturer Databricks. It can be operated on the cloud infrastructure of your choice (Azure, AWS, GCP) and offers maximum flexibility for all types of data and use cases.
SAP Databricks, on the other hand, is a specialized OEM solution that is licensed exclusively via SAP. It is deeply integrated into SAP Datasphere (formerly BDC) and optimized to efficiently process complex SAP data. This solution is aimed specifically at companies that want to maximize the value of their SAP data through advanced analytics and AI applications.
Andreas & Yvonne's Databricks-Guide
Would you like all the important information at a glance?
Download the free guide to SAP Databricks now!
2. direct comparison: SAP Databricks vs. native Databricks
The following table provides a clear overview of the most important differences to make your decision easier.
| Characteristic | SAP Databricks | Native Databricks |
|---|---|---|
| Main purpose | Specialized platform for advanced analytics and AI that focuses primarily on SAP data. | A unified, general platform for data engineering, data science, machine learning and business intelligence. |
| Integration focus | Simplifies the extraction and use of complex SAP data through zero-copy/delta sharing. | An open, flexible platform that integrates with a wide range of data sources, applications and cloud services. |
| Target company | Companies that want to merge their SAP data with other company data for advanced analytics and AI. | Any company with sufficient technical expertise. |
| Target groups | Data Scientists, ML & AI Engineers. | Data Engineers, Data Scientists, ML & AI Engineers, Business Analysts, BI Engineers. |
| Licensing | OEM (Original Equipment Manufacturer) solution as part of the SAP Datasphere subscription with consumption-based licensing via SAP. | SaaS solution purchased directly from Databricks, with cloud storage managed separately. |
| Technology stack | Based on the Databricks platform, supplemented by special connectors, templates and reference architectures for SAP-specific use cases. | The core Databricks platform with Databricks Lakehouse, Delta Lake, MLflow and Apache Spark, designed for all types of data and AI workloads. |
To make the right decision between SAP Databricks and native Databricks, it is crucial to understand the core differences in the following areas:
1st main purpose: The most fundamental difference lies in the intended use. Native Databricks is designed as a universal and comprehensive platform that covers all areas from data engineering and data science to machine learning and business intelligence. In contrast, SAP Databricks is a highly specialized solution that primarily aims to enable advanced analytics and AI applications based on SAP data. It is the tool of choice for maximizing the value inherent in SAP systems.
2. integration focus: This different purpose is reflected in the integration focus. SAP Databricks shines with its ability to use complex SAP data seamlessly and efficiently through native connectors and methods such as zero-copy/delta sharing. The focus is clearly on simplifying access to the SAP data universe. Native Databricks takes an open approach and offers flexible integration options with a wide range of data sources, cloud services and third-party applications.
3. target companies:SAP Databricks is aimed specifically at companies that have already invested heavily in the SAP landscape and want to merge their SAP data with other company data for in-depth analyses and AI projects. Native Databricks is industry and system agnostic and is suitable for any organization that has the technical expertise to implement and manage an open data platform.
4. target groups: The different focus also leads to different primary user groups. As SAP Databricks is designed for highly specialized use cases, the main users are data scientists as well as machine learning and AI engineers. The broader native Databricks platform serves a larger user base that also includes data engineers, business analysts and BI engineers.
5 Licensing: The business model is also fundamentally different. SAP Databricks is licensed exclusively via SAP as an OEM (Original Equipment Manufacturer) solution and is part of the SAP Datasphere subscription. Billing is based on consumption, which offers SAP customers the advantage of a consolidated contract and billing landscape. Native Databricks is a classic SaaS solution (Software-as-a-Service) that is purchased directly from Databricks, whereby the underlying cloud storage (on Azure, AWS or GCP) is managed and billed separately.
6. technology stack: Although both solutions are based on the same core Databricks technology (such as Databricks Lakehouse, Delta Lake and Apache Spark), SAP Databricks offers decisive added value: it supplements this stack with special connectors, predefined templates and reference architectures that are precisely tailored to SAP-specific use cases. This speeds up implementation considerably. Native Databricks offers a pure, flexible technology stack that can be individually configured for any type of data and AI workload.
Andreas & Yvonne's Databricks-Guide
Would you like all the important information at a glance?
Download the free guide to SAP Databricks now!
3. conclusion
The decision between SAP Databricks and native Databricks is not a question of better or worse technology, but a strategic choice.
SAP Databricks is the consistent and logical choice for companies that are deeply rooted in the SAP ecosystem. It offers a seamless, optimized and simplified way to leverage the value of your SAP data through advanced analytics and AI. The advantage lies clearly in the reduced complexity and fast time-to-value thanks to the deep integration.
Native Databricks, on the other hand, is the ideal solution for companies that prefer an open, cloud-agnostic architecture and require maximum flexibility for a heterogeneous data landscape. It offers full control over the technology stack and is perfect for organizations that are not primarily focused on SAP data and have the appropriate technical expertise in-house.
Ultimately, the key question is: are you looking for a specialized solution that harmonizes perfectly with your SAP world, or a universal platform that offers you maximum independence? Your answer will determine the right choice for your data future.
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.
