Data Streaming Meets the SAP Ecosystem and Databricks – Insights from SAP Sapphire Madrid

I had the opportunity to attend SAP Sapphire 2025 in Madrid—an impressive gathering of SAP customers, partners, and technology leaders from around the world. It was a massive event, bringing the global SAP community together to explore the company’s future direction, innovations, and growing ecosystem.

A key highlight was SAP’s deepening integration of Databricks as an OEM partner for AI and analytics within the SAP Business Data Cloud—showing how the ecosystem is evolving toward more open, composable architectures.

At the same time, conversations around Confluent and data streaming highlighted the critical role real-time integration plays in connecting SAP systems (including ERP, MES, DataSphere, Databricks, etc.) with the rest of the enterprise. As always, it was a great place to learn, connect, and discuss where enterprise data architecture is heading—and how technologies like data streaming are enabling that transformation.

Join the data streaming community and stay informed about new blog posts by subscribing to my newsletter and follow me on LinkedIn or X (former Twitter) to stay in touch. And make sure to download my free book about data streaming use cases, focusing on industry scenarios, success stories and business value.

SAP’s Vision: Business Data Cloud, Joule, and Strategic Ecosystem Moves

SAP presented a broad and ambitious strategy centered around the SAP Business Data Cloud (BDC), SAP Joule (including its Agentic AI initiative), and strategic collaborations like SAP Databricks, SAP DataSphere, and integrations across multiple cloud platforms. The vision is clear: SAP wants to connect business processes with modern analytics, AI, and automation.

For those of us working in data streaming and integration, these developments present a major opportunity. Most customers I meet globally uses SAP ERP or other products like MES, SuccessFactors, or Ariba. The relevance of real-time data streaming in this space is undeniable—and it’s growing.

Building the Bridge: Event-Driven Architecture + SAP

One of the most exciting things about SAP Sapphire is seeing how event-driven architecture is becoming more relevant—even if the conversations don’t start with “Apache Kafka” or “Data Streaming.” In the SAP ecosystem, discussions often focus on business outcomes first, then architecture second. And that’s exactly how it should be.

Many SAP customers are moving toward hybrid cloud environments, where data lives in SAP systems, Salesforce, Workday, ServiceNow, and more. There’s no longer a belief in a single, unified data model. Master Data Management (MDM) as a one-size-fits-all solution has lost its appeal, simply because the real world is more complex.

This is where data streaming with Apache Kafka, Apache Flink, etc. fits in perfectly. Event streaming enables organizations to connect their SAP solutions with the rest of the enterprise—for real-time integration across operational systems, analytics platforms, AI engines, and more. It supports transactional and analytical use cases equally well and can be tailored to each industry’s needs.

In the SAP ecosystem, customers typically don’t look for open source frameworks to assemble their own solutions—they look for a reliable, enterprise-grade platform that just works. That’s why Confluent’s data streaming platform is an excellent fit: it combines the power of Kafka and Flink with the scalability, security, governance, and cloud-native capabilities enterprises expect.

SAP, Databricks, and Confluent – A Triangular Partnership

At the event, I had some great conversations—often literally sitting between leaders from SAP and Databricks. Watching how these two players are evolving—and where Confluent fits into the picture—was eye-opening.

SAP and Databricks are working closely together, especially with the SAP Databricks OEM offering that integrates Databricks into the SAP Business Data Cloud as an embedded AI and analytics engine. SAP DataSphere also plays a central role here, serving as a gateway into SAP’s structured data.

Meanwhile, Databricks is expanding into the operational domain, not just the analytical lakehouse. After acquiring Neon (a Postgres-compatible cloud-native database), Databricks is expected to announce an additional own transactional OLTP solution soon. This shows how rapidly they’re moving beyond batch analytics into the world of operational workloads—areas where Kafka and event streaming have traditionally provided the backbone.

This trend opens up a significant opportunity for data streaming platforms like Confluent to play a central role in modern SAP data architectures. As platforms like Databricks expand their capabilities, the demand for real-time, multi-system integration and cross-platform data sharing continues to grow.

Confluent is uniquely positioned to meet this need—offering not just data movement, but also the ability to process, govern, and enrich data in motion using tools like Apache Flink, and a broad ecosystem of connectors, including those for transactional systems like SAP ERP, but also Oracle databases, IBM mainframe, and other cloud services like Snowflake, ServiceNow or Salesforce.

Data Products, Not Just Pipelines

The term “data product” was mentioned in nearly every conversation—whether from the SAP angle (business semantics and ownership), Databricks (analytics-first), or Confluent (independent, system-agnostic, streaming-native). The key message? Everyone wants real-time, reusable, discoverable data products.

This is where an event-driven architecture powered by a data streaming platform shines: Data Streaming connects everything and distributes data to both operational and analytical systems, with governance, durability, and flexibility at the core.

Confluent’s data streaming platform enables the creation of data products from a wide range of enterprise systems, complementing the SAP data products being developed within the SAP Business Data Cloud. The strength of the partnership lies in the ability to combine these assets—bringing together SAP-native data products with real-time, event-driven data products built from non-SAP systems connected through Confluent. This integration creates a unified, scalable foundation for both operational and analytical use cases across the enterprise.

Industry-Specific Use Cases to Explore the Business Value of SAP and Data Streaming

One major takeaway: in the SAP ecosystem, generic messaging around cutting edge technologies such as Apache Kafka does not work. Success comes from being well-prepared—knowing which SAP systems are involved (ECC, S/4HANA, on-prem, or cloud) and what role they play in the customer’s architecture. The conversations must be use case-driven, often tailored to industries like manufacturing, retail, logistics, or the public sector.

This level of specificity is new to many people working in the technical world of Kafka, Flink, and data streaming. Developers and architects often approach integration from a tool- or framework-centric perspective. However, SAP customers expect business-aligned solutions that address concrete pain points in their domain—whether it’s real-time order tracking in logistics, production analytics in manufacturing, or spend transparency in the public sector.

Understanding the context of SAP’s role in the business process, along with industry regulations, workflows, and legacy system constraints, is key to having meaningful conversations. For the data streaming community, this is a shift in mindset—from building pipelines to solving business problems—and it represents a major opportunity to bring strategic value to enterprise customers.

You are lucky: I just published a free ebook about data streaming use cases focusing on industry scenarios and business value: “The Ultimate Data Streaming Guide“.

Looking Forward: SAP, Data Streaming, AI, and Open Table Formats

Another theme to watch: data lake and format standardization. All cloud providers and data vendors like Databricks, Confluent or Snowflake are investing heavily in supporting open table formats like Apache Iceberg (alongside Delta Lake at Databricks) to standardize analytical integrations and reduce storage costs significantly.

SAP’s investment in Agentic AI through SAP Joule reflects a broader trend across the enterprise software landscape, with vendors like Salesforce, ServiceNow, and others embedding intelligent agents into their platforms. This creates a significant opportunity for Confluent to serve as the streaming backbone—enabling real-time coordination, integration, and decision-making across these diverse, distributed systems.

An event-driven architecture powered by data streaming is crucial for the success of Agentic AI with SAP Joule, Databricks AI agents, and other operational systems that need to be integrated into the business processes. The strategic partnership between Confluent and Databricks makes it even easier to implement end-to-end AI pipelines across the operational and analytical estates.

SAP Sapphire Madrid was a valuable reminder that data streaming is no longer a niche technology—it’s a foundation for digital transformation. Whether it’s SAP ERP, Databricks AI, or new cloud-native operational systems, a Data Streaming Platform connects them all in real time to enable new business models, better customer experiences, and operational agility.

Join the data streaming community and stay informed about new blog posts by subscribing to my newsletter and follow me on LinkedIn or X (former Twitter) to stay in touch. And make sure to download my free book about data streaming use cases, focusing on industry scenarios, success stories and business value.