en.Wedoany.com Reported - On July 7, German enterprise software company SAP SE announced the completion of its acquisition of Dremio, a US-based data lakehouse platform provider. The deal integrates Dremio's open, high-performance data lakehouse capabilities into SAP's data and artificial intelligence ecosystem, aiming to enhance customers' ability to unify SAP system data with non-SAP system data, while supporting real-time analytics and AI workloads. SAP stated that these capabilities enable enterprises to use data without moving or transforming it, improving the cost structure of enterprise analytics.
The focus of this acquisition goes beyond adding a data product; it addresses the most challenging data foundation issues when enterprise AI applications move into production environments. Large enterprises typically have data scattered across ERP, supply chain, finance, human resources, customer relationship management, manufacturing execution systems, cloud services, and external business platforms. Data resides in different locations, formats, permissions, and business meanings. For AI agents to truly participate in order analysis, procurement decisions, inventory forecasting, financial risk identification, or customer operations, they must not only call models but also understand data sources, business object relationships, permission boundaries, and result credibility. Germany's SAP acquiring US-based Dremio aims to integrate these capabilities on top of core enterprise business systems.
Dremio's core strength lies in its open data lakehouse architecture. The data lakehouse combines the massive storage capacity of a data lake with the query and analytics capabilities of a data warehouse, enabling enterprises to manage structured, semi-structured, and multi-source business data in a more open architecture. For enterprise customers, the key value lies in reducing data replication, extraction, transformation, and repeated modeling. In the past, many enterprises had to move data from different systems into new warehouses or platforms for unified analytics, followed by cleaning and modeling. The longer the process, the higher the latency, the greater the governance costs, and the more likely new data silos would form. With Dremio integrated into SAP's ecosystem, SAP customers can directly query, analyze, and invoke AI around original data locations, reducing the complexity of data engineering pipelines.
SAP Business Data Cloud is the primary platform for this integration. Germany's SAP previously stated that Dremio will complement SAP Business Data Cloud and SAP HANA Cloud, enabling enterprises to unify SAP and non-SAP data and run analytics and AI workloads in real-time scenarios. For SAP customers, this means data from core business systems like ERP can be more tightly integrated with cloud platforms, third-party business systems, and external data sources for joint analysis, rather than remaining in isolated data domains.
Open ecosystem is also a significant signal in this deal. Dremio has long participated in open-source table format projects like Apache Iceberg, open-source catalog projects like Apache Polaris, and cross-language data exchange projects like Apache Arrow. When Germany's SAP disclosed the acquisition plan in May, it stated that it would continue to invest in and prioritize support for these open-source projects. Competition in enterprise data platforms is shifting from single database performance to collaboration among open table formats, data catalogs, federated queries, semantic layers, and AI-consumable data. If SAP can maintain Dremio's open approach, it can reduce customer concerns about single closed systems and facilitate the inclusion of more non-SAP data into a unified governance environment.
Agentic AI places higher demands on data foundations. While regular reports can tolerate manual supplementary explanations, AI agents need to determine field meanings, invoke business rules, execute analytical actions, and return interpretable results in a shorter time. For example, if a supply chain department asks an AI agent to identify delivery risks, the system cannot just read an order table; it must understand relationships among suppliers, logistics nodes, inventory, production plans, payment terms, and historical anomaly records. Without unified semantics and governance capabilities, AI agents can easily produce conclusions that seem reasonable but are not actionable.
This is why Germany's SAP continues to strengthen its enterprise AI strategy. SAP's advantage lies in covering a wide range of core enterprise processes, with business data of high value and complexity; Dremio's advantage lies in open lakehouse, federated query, and high-performance analytics capabilities. By combining the two, SAP can process business semantics, real-time transaction data, external data, and AI workloads within a more unified technical framework, further improving the usability of enterprise-grade AI products.
The transaction amount was not disclosed. The key going forward is not the completion of the acquisition itself, but the speed of product integration, customer migration paths, compatibility with existing SAP data platforms, fulfillment of open-source commitments, and actual enterprise deployment costs. Competition in the enterprise data platform market is fierce, with US-based cloud data platform company Snowflake, US-based data and AI platform company Databricks, and multiple cloud service providers all vying for the data gateway in the AI era. Whether Germany's SAP can transform open lakehouse capabilities into AI data infrastructure that SAP customers can directly deploy will determine the actual value of this deal.
As enterprise AI moves from pilots to business processes, the importance of data platforms will continue to rise. Model capabilities are only part of intelligence; whether data can be accessed in real-time, accurately interpreted, compliantly used, and cost-effectively analyzed is becoming a watershed for enterprise AI adoption. Germany's SAP acquiring US-based Dremio sends a signal: competition in enterprise software has extended from the application layer to the data foundation, and the integration of data lakehouses, semantic governance, real-time analytics, and AI agent execution environments will become a key direction for the subsequent industry chain.
