SAP to acquire Dremio to unify SAP and non-sap data to power agentic AI. PR Newswire Today at 4:17am PDT WALLDORF, Germany and AUSTIN, Texas, May 4, 2026 /PRNewswire/ - SAP SE (NYSE: SAP) and Dremio today announced that SAP has agreed to acquire Dremio, an open, high-performance data lakehouse platform built to accelerate agentic AI and expand SAP Business Data Cloud's ability to combine SAP and non-SAP data to more effectively run analytical and AI workloads in real time. Terms of the deal were not disclosed. The transaction is still pending regulatory approval. Most enterprise AI projects fail to deliver value not because of the AI itself, but because the underlying data is fragmented, locked in proprietary formats and stripped of the business context that makes it meaningful. The result is a familiar and costly pattern: pilots that cannot scale, slow integration of new data sources, duplicated engineering work and compliance risk when organizations cannot explain how an AI-driven decision was reached. Dremio helps eliminate that data fragmentation and integration friction. The acquisition will complement the SAP Business Data Cloud and SAP HANA Cloud offerings to ensure seamless data integration across SAP and non-SAP data with high performance and low cost to accelerate AI-ready context and time-to-value for AI. "Enterprise AI doesn't stall because the models aren't good enough; it stalls because the data isn't ready for AI agents," said Philipp Herzig, CTO, SAP SE. " Dremio eliminates that bottleneck. Combined with SAP Business Data Cloud, we can now take customers from raw, fragmented data to governed, AI-ready intelligence on a single open platform." With Dremio, SAP Business Data Cloud will become an Apache Iceberg-native enterprise lakehouse that unifies SAP and non-SAP data to power agentic AI at enterprise scale. Apache Iceberg is the industry-standard open table format, and SAP Business Data Cloud will natively support it as its foundation. This means no data movement or format conversion will be necessary. SAP and non-SAP data can coexist on the same open foundation, with federated analytical reach across every enterprise data source, combined with SAP HANA Cloud's in-memory engine for real-time transactions and operational performance. The Dremio lakehouse platform is set to vastly improve the economics of enterprise analytics. It is serverless and elastic, scaling up automatically when demand spikes and scaling back down when it subsides, meaning no fixed capacity to provision and no performance ceiling when it matters most. With Dremio, SAP will deliver a universal, open catalog built on Apache Polaris and the open Apache Iceberg REST Catalog API. It serves as both the discovery and semantic layer of SAP Business Data Cloud, giving every connected engine - SAP or non-SAP - a single point of access to unified business context: meaning, relationships, access rights and data lineage. This catalog will form the foundation of the SAP Knowledge Graph, embedding business relationships, organizational hierarchies, regulatory classifications and cross-system lineage as native properties. Dremio has been a leading steward of open-source projects at the heart of its platform: Apache Iceberg, Apache Polaris and Apache Arrow,- and SAP is fully committed to continuing to invest in and prioritize these contributions. The transaction is expected to close in Q3 of 2026, subject to customary closing conditions, including regulatory approvals. About Dremio Dremio is the Agentic Lakehouse: the only Iceberg-native data platform built for agents and managed by agents. Every knowledge worker and AI agent gets instant, governed access to enterprise data through any LLM or tool of their choice. Federated queries reach any source without ETL pipelines. An AI Semantic layer adds business context so every agent draws from the same source of truth. The lakehouse manages itself, running clustering, optimization, and compaction autonomously. The result: trusted insights that drive better business outcomes, without the infrastructure complexity or overhead. A lead contributor to Apache Iceberg and co-creator of Apache Arrow and Apache Polaris. Trusted by Shell, TD Bank, Michelin, and thousands of organizations worldwide. https://www.dremio.com/ About SAP As a global leader in enterprise applications and business AI, SAP (NYSE: SAP) stands at the nexus of business and technology. For over 50 years, organizations have trusted SAP to bring out their best by uniting business-critical operations spanning finance, procurement, HR, supply chain, and customer experience. For more information, visit www.sap.com. This document contains forward-looking statements, which are predictions, projections, or other statements about future events. These statements are based on current expectations, forecasts, and assumptions that are subject to risks and uncertainties that could cause actual results and outcomes to materially differ. Additional information regarding these risks and uncertainties may be found in our filings with the Securities and Exchange Commission, including but not limited to the risk factors section of SAP's 2025 Annual Report on Form 20-F. (C) 2026 SAP SE. All rights reserved. SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered trademarks of SAP SE in Germany and other countries. Please see https://www.sap.com/copyright for additional trademark information and notices. Note to editors: To preview and download broadcast-standard stock footage and press photos digitally, please visit www.sap.com/photos. On this platform, you can find high resolution material for your media channels. For customers interested in learning more about SAP products: Global Customer Center: +49 180 534-34-24 United States Only: 1 (800) 872-1SAP (1-800-872-1727) SOURCE SAP SE This is a paid placement. For further inquiries, please contact PR Newswire directly.

Dremio wants to turn Iceberg's open-format victory into a simpler lakehouse pitch. 8 April, 2026 As Apache Iceberg becomes the default table format for more AI and analytics workloads, Dremio is arguing that the real challenge is no longer adoption, but the operational burden that comes after it. Apache Iceberg has effectively won the table-format wars, and Dremio is using that moment to make a sharper case for its own platform: the hard part now is not choosing an open format, but managing it without adding new layers of cost and complexity. Dremio argues that enterprises embraced Iceberg because they wanted interoperability and less lock-in, and that the format has also become increasingly important for AI-era data architectures that need access to structured, semi-structured and unstructured data in one lakehouse. Why this matters for users. For users, the promise of Iceberg is flexibility. Teams can keep data in object storage, use multiple engines, and avoid getting trapped inside a single vendor's proprietary format. But Dremio's post makes the point that openness brings its own operational tax: Iceberg tables fragment over time, metadata grows, snapshots pile up, and performance can degrade unless engineers actively compact files, tune layouts and schedule maintenance jobs. For many data teams, that means time that should go toward new data products, models or business analysis instead gets spent babysitting tables. Dremio's competitive angle is automation. That is where Dremio tries to distinguish itself from competitors like Snowflake and Databricks. The company says it was built around Iceberg from the ground up, rather than adding support later, and is pitching itself as the platform that automates the parts of Iceberg management that users least want to do manually. According to Dremio, its platform continuously optimizes physical data layout with Iceberg Clustering, automatically adapts query acceleration through Autonomous Reflections, and handles file compaction, snapshot expiration, manifest rewriting and orphan file cleanup without manual scheduling. Dremio explicitly contrasts that with Databricks, where it says customers still manage optimization jobs themselves, and with Snowflake, where it says automation is more limited for Snowflake-managed Iceberg tables. The value proposition for customers is straightforward: lower operational overhead and better performance without dedicated maintenance work. Dremio says its autonomous optimization reduces the need for full table rewrites by targeting only degraded regions of data layout, while its reflections system materializes only what is needed based on observed query behavior. The company says this can replace more complex silver-and-gold ETL layering with a more virtualized approach and claims query speeds up to 20 times faster than competing lakehouses on TPC-DS benchmarks. That kind of message is aimed directly at teams that like Iceberg's openness but miss the more hands-off performance tuning of classic cloud warehouses. Interoperability is still the main strategic message. Dremio is also leaning hard on openness as a competitive weapon. The company says it co-founded Apache Polaris, an open catalog standard, and argues that this helps customers avoid a new kind of lock-in at the catalog layer. In the post, Dremio says every table it manages is accessible through compatible engines such as Spark, Trino, Flink, DuckDB and Dremio itself. It contrasts that with Databricks' Unity Catalog-centric approach and Snowflake's managed-table model. For customers building AI and analytics systems across multiple engines and frameworks, Dremio argues that open access to data and metadata is no longer optional. Why Iceberg V3 could matter more than it sounds. The company also uses the post to highlight Apache Iceberg V3, which it describes as the biggest upgrade since row-level deletes in V2. Dremio says it has already shipped V3 table read and write support, including binary deletion vectors that can make updates and deletes faster and less compute-intensive than older position-delete approaches. It also points to new row-level lineage fields, the VARIANT type for semi-structured data, and nanosecond-precision timestamps as features that make Iceberg more suitable for real-time analytics, CDC pipelines, financial services and IoT workloads. Dremio's argument is that these are not incremental additions but features that make Iceberg more practical for the next generation of AI-heavy data systems. What Dremio is really selling. Underneath the format-war framing, Dremio is really making a broader pitch about the future of the lakehouse. It is saying that openness alone is not enough; the winning platform will be the one that keeps Iceberg interoperable while removing the management burden that often comes with it. That gives Dremio a different position from vendors that support Iceberg but still steer customers toward proprietary catalogs, managed layers or heavier operational involvement. Image: Dremio

Dremio, the Agentic Lakehouse company, has announced several developments strengthening its position in the Apache Iceberg ecosystem. The company now offers Apache Iceberg V3 support in Dremio Cloud, featuring deletion vectors for faster change data capture, the VARIANT data type for JSON, and enhanced schema evolution capabilities. Dremio engineer JB Onofre has been elected to the Apache Software Foundation board, following his role shepherding Apache Polaris through incubation. Dremio co-created Polaris, which has now graduated to a top-level Apache project and powers the company's Open Catalog feature. The company's platform includes autonomous reflections for query optimisation, Iceberg clustering using Z-order, and automatic table maintenance. Dremio supports full read and write operations across REST-compatible engines including Spark, Flink, Trino and DuckDB.