# SAP x Databricks

> A Game-Changer? We Will See

**Published by:** [ByteByByte](https://bytebybyte.tech/)
**Published on:** 2025-02-15
**Categories:** sap, databricks, sap databricks partnership, enterprise data strategy, tech partnerships, data governance
**URL:** https://bytebybyte.tech/sap-x-databricks

## Content

I’ve been working with companies on migrating to or building brand-new data platforms in Databricks since 2018 across various cloud providers. From my experience, Databricks (DBx) has always been a leader in the data and AI platform space. Its unified Lakehouse architecture streamlines data storage, ETL, and AI/ML workflows in a way that many traditional tools struggle to match. With Serverless Compute's recent GA release last year, Databricks manages and allocates compute for rapid start-up times and reduced overhead, enhancing user productivity. Databricks performance is excellent too—its Photon engine significantly cuts query times and reduces cloud costs—and the built-in AI/ML features, like MLflow and GPU acceleration, are tough to beat at enterprise scale. With Unity Catalog providing centralized governance across multiple clouds, Databricks makes security and access control much easier. Of course, the competition is catching up, and many are close to feature parity in some areas. Still, from my experience, DBx holds a strong edge for data and AI-centric workloads—at least for the moment.The Big News: SAP Teams Up with DatabricksRecently, SAP and Databricks announced a partnership to launch (GA TBD) what they’re calling “SAP Databricks”. This integrated solution will combine Databricks’ Data Intelligence Platform with SAP’s Business Data Cloud. The aim? To unify SAP and third-party data into an enterprise-ready data foundation for advanced analytics and AI use cases. Naturally, I’m intrigued—and a bit cautious. While I'm not an SAP expert by any means, this collaboration could be a huge step forward for organizations that rely on SAP’s deep ERP capabilities but also want Databricks’ best-in-class analytics. However, as with any big tech partnership, the actual benefits will depend on how well it all comes together in the real world once it's generally available.What's the Potential Upside?Ease of Data Sharing: Traditionally, SAP environments have been somewhat walled off, with data siloed inside ERP systems. This partnership should let companies merge their SAP data with other datasets. The result could be a more cohesive, analytics-ready data ecosystem—potentially cutting down on various, complex, spaghetti sets of ETL pipelines.AI-Driven Launch Pad: By bringing Databricks’ data engineering and AI capabilities into SAP’s Business Data Cloud, it should become much easier to run advanced AI/ML models on SAP data. Think about predictive maintenance in manufacturing, real-time fraud detection in banking, or smarter demand forecasting in retail. This integration ideally would accelerate the AI-powered, domain-specific application/use case development that historically have been challenging to implement (for many reasons).Governance at Scale: Databricks’ Unity Catalog enables governance and compliance and that will now be available (or should be) across both SAP and non-SAP data (with some work to set up the infrastructure). For highly regulated industries—like finance or healthcare—this unified approach is very important. It’s one of those “must-haves” in a world where data breaches and compliance violations are all too common.Again, we will see how the actual rollout goes once this is GA available. Depending on the integration approach, some of these benefits might be at risk.Where's the Risk?Despite the promising outlook, there are still plenty of challenges ahead. Here are a few big ones:Integration Complexity: SAP systems are often deeply entangled with core business processes. Integrating them with the Databricks platform isn’t just a matter of flipping a switch. Companies will need careful planning, strong data governance, and well-trained teams. A poorly executed migration could lead to data inconsistencies—or worse, operational hiccups.Legacy Systems & Cost: SAP workloads frequently live on-prem or in older cloud versions due to challenges with migrating to the latest/greatest version of SAP, which means pulling data into Databricks can add significant egress costs and latency issues or companies will need to undergo cloud migration efforts before they can achieve the benefits of DBx and SAP's partnership. Real-time analytics, in particular, could suffer if organizations don’t architect things properly. Financial and performance trade-offs must be thoroughly evaluated before diving in. Additionally, what will licensing look like for this new model and will companies sign up for those agreements. Skill Gaps and Change Management: SAP professionals often stick to the SAP ecosystem, while Databricks expertise typically resides with cloud data engineers and data scientists. Bridging this skills gap is no small feat. Without the right training and a shift in data culture, the best technology in the world won’t yield the desired business results. This could be a bonus however as DBx is well known, python is well known and it could open up the pool of developers who can work with these SAP-focused companies. What Should Companies Do Now?A few thoughts for all those organizations that now have (or will soon) DBx at your fingertips.Wait & See: Obviously, you'll need to work closely with SAP and Databricks on this—currently there is a waitlist and how all the details come together will be key. Stay updated on announcements from both companies and watch for insights from early adopters to gauge real-world impact and challenges. Evaluate Your AI & Analytics Maturity: If you already have a solid AI/ML strategy, it might be worth exploring how to integrate SAP data into your Databricks environment and/or considering how DBx can uplevel what you currently have. At least start the discussion. Assess Infrastructure & Costs: Make sure you explore how data processing will work with the partnership. Any move of SAP workloads to DBx needs to fit your overall cloud strategy—both technically and financially. Egress costs, inefficient workloads, and latency are real concerns.Prioritize Governance & Security: Before diving in, confirm that your data governance frameworks can handle cross-platform integrations. Security shouldn’t be an afterthought.Invest in Skill Development: Training and cross-pollinating teams (SAP folks learning Databricks, Databricks folks learning SAP) will make for a smoother transition and help unlock the full value of this partnership.Final ThoughtsThe SAP-Databricks partnership signals a shift toward more data sharing (fewer silos) and data science forward strategies (no surprise, we've been seeing this shift for years and it's only going to speed up with GenAI). It has the potential to accelerate AI adoption, streamline operations, and give companies a more holistic view of their data. But as always, the devil is in the details: strategic planning, disciplined execution, and a willingness to adapt will determine whether this partnership truly lives up to the hype—not just for these companies but also for SAP and DBx as it relates to the integration and rollout. As this starts rolling out to more and more customers, it will be interesting to see how it's received. I'm looking forward to seeing how it goes over the next several months. I want to give a shout to Taylor who helped me prep this Blog and provide some great technical and flow recommendations as well - thanks Taylor! P.S. Happy Valentine's Day all! P.P.S The picture at the top is of the sunrise this morning. This picture is a fun sign I've seen and admired in my neighborhood for a while - Pepsi, candy, and cigars...what a way to live!

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