On Monday, SAP announced its intention to acquire Dremio, a company known for its agentic lakehouse technology. While the purchase price remains undisclosed, the move introduces new dynamics in the enterprise data landscape, particularly given SAP's existing partnerships with Snowflake and Databricks. This Q&A explores the rationale behind the acquisition, how Dremio differentiates itself, and what this means for IT leaders seeking to unify SAP and non-SAP data for AI initiatives.
1. What is Dremio, and why is SAP buying it?
Dremio is a data lakehouse provider that specializes in making complex data more AI-ready without requiring data movement or format conversion. SAP's primary justification for the acquisition is to simplify combining SAP data with non-SAP data, especially by leveraging Dremio's ability to work with data that remains within the enterprise's own environment. This contrasts with solutions that force data to live externally. SAP highlights that many enterprise AI projects fail due to fragmented, locked-down data lacking business context. By integrating Dremio, SAP aims to eliminate data fragmentation and integration friction, enabling faster, more cost-effective AI deployment at scale. Internal anchor: How does Dremio differ from Snowflake and Databricks?
2. How does Dremio differ from existing SAP partners like Snowflake and Databricks?
Analysts note that comparing Dremio with Snowflake or Databricks is nuanced. Dremio is younger and less established, which may raise questions about enterprise readiness. However, its key differentiator is the ability to query data in place, without moving it to an external platform. This native adherence to Apache Iceberg allows SAP and non-SAP data to coexist on a common open foundation. In contrast, Snowflake and Databricks often require data to be ingested into their own systems. Additionally, SAP strategy expert Harikishore Sreenivasalu points out that acquiring Snowflake or Databricks would have been prohibitively expensive today, making Dremio a more practical target despite its need to mature in areas like security and multi-cloud support.
3. How does Dremio specifically address data fragmentation?
Dremio tackles fragmentation by enabling federated queries across disparate data sources without moving or copying data. SAP explains that with Dremio, the SAP Business Data Cloud becomes an Apache Iceberg-native enterprise lakehouse. This means all data—SAP and non-SAP—sit on the same open table format, eliminating the need for format conversion or constant data movement. The result is a unified environment where analysts and AI models can access diverse datasets seamlessly. This approach reduces duplicated engineering work and compliance risks because data retains its original context. However, SAP acknowledges that Dremio does not fix every data quality issue—such as outdated or unreliable sources—but it significantly eases integration friction.
4. Is Dremio enterprise-ready compared to Snowflake or Databricks?
According to consultant Harikishore Sreenivasalu, Dremio is still maturing. Snowflake and Databricks have more mature platforms, better multi-cloud support, and stronger security features. Dremio, as a newer entrant, must prove its enterprise credentials. However, the situation could evolve post-acquisition with SAP's resources. For now, enterprises that prioritize data locality and minimal data movement may find Dremio's approach compelling, even if its overall enterprise readiness is not on par with the established players. The acquisition may accelerate Dremio's maturity in security and multi-cloud capabilities.
5. What does this acquisition mean for SAP's data strategy?
The acquisition positions SAP to offer a more unified data platform, especially for customers who want to blend SAP transactional data with external sources for AI. By embedding Dremio's technology, SAP aims to reduce the friction that often derails AI pilots. The Apache Iceberg-native lakehouse approach promises scalability and openness. This move also signals SAP's commitment to agentic AI—AI that can act autonomously—by providing a solid data foundation. Existing SAP partners like Snowflake and Databricks may need to adapt, but the acquisition is unlikely to disrupt current relationships immediately.
6. What are the risks of this acquisition?
One risk is that Dremio's relative immaturity could slow adoption among conservative enterprises. Integrating a new platform into SAP's sprawling ecosystem may also present technical challenges. Additionally, the acquisition could strain relationships with partners Snowflake and Databricks, who now compete more directly with SAP's own lakehouse. There's also the question of whether Dremio can deliver on its promise to solve all data fragmentation issues. As SAP itself notes, many aspects of data quality—like timeliness and source reliability—remain unaddressed. Finally, the unspecified price leaves uncertainty about ROI, though analysts believe it's lower than acquiring a larger player.
7. Key takeaways for IT leaders
IT leaders should watch how SAP integrates Dremio into the Business Data Cloud. If successful, it could simplify data lakehouse architectures by removing the need for separate ETL processes. For AI projects, this means faster time-to-value and reduced compliance overhead. However, due diligence is needed: evaluate whether Dremio's security and multi-cloud support meet your organization's requirements. Those heavily invested in Snowflake or Databricks should monitor SAP's roadmap to understand potential synergies or conflicts. Ultimately, this acquisition reinforces the trend toward open table formats like Apache Iceberg and the importance of data locality in AI strategies.