From Monolithic Stacks to Open Lakehouse: Why SAP Needed Dremio
SAP’s acquisition of Dremio is a strategic move to merge an open, high‑performance lakehouse platform with SAP Business Data Cloud so enterprises can unify SAP and non‑SAP data for real-time analytics and agentic AI on a single, open foundation without moving or converting data, signalling a decisive shift away from monolithic data silos toward modular, interoperable data architectures.
SAP has completed its purchase of Dremio, bringing an open data lakehouse platform directly into its core data stack. By doing this, SAP is not just adding another database option; it is admitting that classic, closed analytics architectures are no longer enough for enterprise AI. The deal strengthens SAP Business Data Cloud with a lakehouse platform enterprise customers can use across SAP and non-SAP systems, without the drag of constant data copying or format translation.
This matters because AI projects fail less from weak models and more from weak data plumbing. As SAP’s CTO Philipp Herzig puts it, “Enterprise AI doesn’t stall because the models aren’t good enough; it stalls because the data isn’t ready for AI agents.” Dremio is SAP’s answer to that bottleneck, and the timing confirms that the real frontier of AI is data modernization, not yet another model.
Agentic AI Demands a Different Data Foundation
Agentic AI data modernization is not about sprinkling models over legacy warehouses; it is about giving autonomous AI agents governed, live access to enterprise-wide data in a form they can query, explain, and act on continuously.
Dremio was built to query data where it lives, without heavy ETL pipelines or extensive replication. That is exactly what agentic AI needs: a shared, open data layer the agents can tap in real time. By combining Dremio with SAP Business Data Cloud and SAP HANA Cloud, SAP is promising federated analytical access across sources plus in‑memory performance for transactional and analytical workloads in the same architecture. In practice, that means AI agents can work off both operational and historical data streams without waiting for overnight batch jobs.
This is also a bet on real-time analytics integration as the new default. The combined platform is designed so customers can run analytical and AI workloads in real time with no data movement or conversion, and with better economics for enterprise analytics. For teams tired of stitching together brittle pipelines just to keep dashboards alive, this is a clear signal: the data platform should carry that burden, not individual projects.
Open Lakehouse Platforms Are Replacing Proprietary Data Silos
The most underestimated part of this deal is SAP’s embrace of an Apache Iceberg‑native enterprise lakehouse as the backbone of SAP Business Data Cloud. This is SAP conceding that the future belongs to modular, open data platforms, not closed, vertically integrated stacks.
Apache Iceberg has become the de facto open table format for large-scale analytics because it lets organisations store and manage data without locking into proprietary storage systems. By adopting this architecture natively, SAP is allowing SAP and non‑SAP data to coexist on a shared open foundation, eliminating the need for data movement or conversion before analytics or AI processing can start. That directly attacks the long-standing pain of fragmented, disconnected data that slows down AI adoption and raises compliance risks.
SAP is also committing to continued investment in the open-source technologies behind Dremio, including Apache Iceberg, Apache Polaris, and Apache Arrow. This is not a cosmetic partnership; it is a structural shift. SAP is effectively saying the winning lakehouse platform enterprise strategy is to plug into the open ecosystem that AI teams already trust, rather than to pull those teams into yet another proprietary island.
Unified Data for Agentic AI: What Changes for Enterprises
For ordinary users inside large organisations, this acquisition is about speed and clarity more than architecture. By tackling fragmented data estates and proprietary formats, SAP wants to help organisations accelerate analytical workloads and deploy AI at scale with greater efficiency and lower cost.
Dremio’s lakehouse platform will complement SAP Business Data Cloud and SAP HANA Cloud to deliver seamless integration across SAP and non‑SAP environments while still supporting real-time analytical and AI workloads. That means AI agents can operate on a unified data layer that spans structured ERP tables and unstructured or semi‑structured sources, enabling faster decision-making on top of a single, governed view of the business. The platform’s serverless, elastic design also scales compute automatically based on demand, improving the economics of analytics as data and AI usage grow.
Equally important is governance. A universal, open data catalog built on Apache Polaris and the Apache Iceberg REST Catalog API will give analytical engines a shared semantic layer, including relationships, access rights, and lineage. This will feed SAP’s Knowledge Graph, embedding organisational hierarchies and regulatory context directly into the platform. For AI teams, that translates into more explainable outcomes and a cleaner path through emerging compliance requirements.
The Bigger Signal: Data Modernization Is Now AI Strategy
With the transaction closed, SAP has expanded its ability to offer an open, AI‑ready data platform that blends business context, real-time analytics, and simplified data integration. The message to the market is blunt: data modernization is no longer a side project; it is the main path to production AI.
The lakehouse platform enterprise pattern—open table formats, federated query, elastic compute, and a shared semantic layer—is becoming the default stack for agentic AI data modernization. SAP is now aligning its SAP data cloud strategy with that pattern instead of fighting it. Organisations that cling to tightly coupled warehouses and point ETL will find themselves unable to support autonomous AI agents that need live, explainable, cross‑system context.
The real test will be execution: can SAP keep Dremio’s openness intact while deeply integrating it with its existing products? If it can, the payoff is significant. Enterprises will move from talking about AI pilots to running AI agents on top of a unified, governed, hybrid data layer. In that world, buying yet another model matters less than getting the data foundation right—and SAP’s Dremio move is a clear recognition of that reality.
