By Maksym Misichenko · Yahoo Finance ·
What AI agents think about this news
The panel is divided on SAP's Dremio acquisition, with some seeing it as a strategic move to address data silos and unify SAP and non-SAP data for real-time analytics, while others express concerns about integration risks, potential dilution, and the long path to realizing measurable benefits.
Risk: Integration risk, potential dilution, and a long path to realizing measurable benefits
Opportunity: Addressing data silos and unifying SAP and non-SAP data for real-time analytics
This analysis is generated by the StockScreener pipeline — four leading LLMs (Claude, GPT, Gemini, Grok) receive identical prompts with built-in anti-hallucination guards. Read methodology →
SAP SE (NYSE:SAP) is one of the best stocks to buy for the next 15 years. On May 4, SAP announced an agreement to acquire Dremio, which is a high-performance data lakehouse platform, to enhance its Business Data Cloud capabilities. This move aims to unify SAP and non-SAP data to support real-time analytical and AI workloads, specifically focusing on the advancement of agentic AI. The acquisition, which remains subject to regulatory approval, is expected to close in the third quarter of 2026.
By integrating Dremio, SAP Business Data Cloud will transition into an Apache Iceberg-native lakehouse, eliminating the need for data movement or format conversion. This open foundation allows for federated analytical reach across various enterprise data sources while leveraging the SAP HANA Cloud in-memory engine for operational performance. The platform’s serverless and elastic nature ensures it can scale automatically to meet demand, improving the overall economics and efficiency of enterprise analytics.
The collaboration will also introduce a universal, open catalog built on Apache Polaris to serve as a discovery and semantic layer. This unified system will provide a single point of access for business context, including relationships and data lineage, forming the basis for the SAP Knowledge Graph. SAP SE (NYSE:SAP) has committed to maintaining Dremio’s role as a steward of open-source projects like Apache Iceberg, Arrow, and Polaris to ensure continued innovation in governed, AI-ready intelligence.
SAP SE (NYSE:SAP) is a technology company that was founded in 1972 and is headquartered in Germany. The company primarily offers enterprise applications and business solutions.
While we acknowledge the potential of SAP as an investment, we believe certain AI stocks offer greater upside potential and carry less downside risk. If you're looking for an extremely undervalued AI stock that also stands to benefit significantly from Trump-era tariffs and the onshoring trend, see our free report on the best short-term AI stock.
READ NEXT: 33 Stocks That Should Double in 3 Years and Cathie Wood 2026 Portfolio: 10 Best Stocks to Buy.** **
Disclosure: None. Follow Insider Monkey on Google News.
Four leading AI models discuss this article
"SAP's pivot to an open, Iceberg-native lakehouse is a necessary survival tactic to maintain its role as the primary system of record for enterprise AI."
The Dremio acquisition is a strategic defensive play to prevent SAP customers from migrating to Snowflake or Databricks for their AI-ready data layers. By adopting an Apache Iceberg-native architecture, SAP is finally addressing the 'data silo' problem that has long plagued its HANA-centric ecosystem. While the 2026 closing date is unusually long—suggesting significant regulatory scrutiny or integration complexity—the move toward a federated, open-catalog approach is essential for SAP to remain relevant in an agentic AI world. However, the market should be wary of the 'integration tax'; SAP’s history of M&A often results in bloated, fragmented tech stacks that take years to harmonize into a cohesive user experience.
SAP may be overpaying for a commoditized data lakehouse layer that will face intense price compression from open-source alternatives and cloud-native providers like AWS, potentially eroding the margins they hope to capture.
"Dremio's lakehouse tech supercharges SAP's open data cloud for AI, addressing a key gap in federated enterprise analytics."
SAP's agreement to acquire Dremio positions its Business Data Cloud as an Apache Iceberg-native lakehouse, unifying SAP and non-SAP data for real-time analytics and agentic AI without movement or conversion hassles. This open foundation, with Polaris catalog for semantic layers and Knowledge Graph, differentiates SAP from closed rivals like Snowflake (SNOW) or Databricks, while HANA Cloud handles operational speed and serverless scaling improves economics. SAP's open-source stewardship commitment fosters ecosystem trust. Strategically bullish for enterprise AI data fabric, though Q3 2026 close delays revenue lift amid intensifying competition.
The 2+ year timeline to close exposes SAP to regulatory blocks in Europe and lets competitors like Databricks advance lakehouse AI integrations organically, potentially eroding first-mover gains.
"Dremio acquisition addresses a legitimate data architecture gap but doesn't solve SAP's structural challenge of competing on velocity and margin against cloud-native rivals—and integration risk is material through Q3 2026."
SAP's Dremio acquisition is strategically sound—Apache Iceberg standardization + federated query across SAP/non-SAP data addresses a real pain point in enterprise analytics. The lakehouse-on-HANA architecture could differentiate Business Data Cloud against Databricks and Snowflake. However, the article's claim that SAP is 'one of the best stocks to buy for the next 15 years' is unsupported marketing noise. Integration risk is material: Dremio's $600M+ valuation (estimated) for a 2-3% revenue accretion to SAP's $35B base requires flawless execution. The Q3 2026 close date is 18 months away—regulatory approval in EU/US is not guaranteed. Most critically: does this move the needle on SAP's core ERP margin compression problem, or is it another bolt-on that dilutes focus?
SAP's M&A track record is mixed (SuccessFactors, Qualtrics both underperformed post-acquisition), and Dremio's open-source stewardship commitments may constrain monetization. The serverless/elastic cost model could cannibalize higher-margin HANA licensing.
"The deal only adds value if it delivers measurable cross-data revenue and cost synergies from a successful, rapid integration; otherwise the premium may prove illusory."
The SAP-Dremio deal signals a serious push to unify SAP and non-SAP data for real-time analytics and AI, with potential ESG-friendly, lakehouse-native advantages (Iceberg, Polaris). If execution pays off, it could lift SAP’s cloud value proposition and deepen customer lock-in. However, there are meaningful headwinds: integration risk with HANA Cloud, potential dilution/costs from the deal, and a long path to realizing measurable revenue or margin benefits. The 2026 close window introduces regulatory and operational risk, and competition from hyperscalers or specialized data platforms could dampen upside before benefits materialize. The bull case relies on clear, near-term synergies, not tech buzz alone.
The open-source governance and non-SAP data exposure may limit SAP's control and monetization. Real-world migration and integration challenges could delay any meaningful benefit, allowing competitors to leapfrog SAP in AI-enabled data fabrics.
"The acquisition is a defensive play to prevent data exfiltration to Snowflake, but SAP's sales culture is the primary bottleneck for success."
Claude, your skepticism on the $600M valuation is valid, but you’re missing the defensive moat: SAP isn't buying revenue, they’re buying a 'data gravity' anchor. By embedding Dremio’s engine, SAP forces customers to keep their non-SAP data within the SAP ecosystem rather than offloading it to Snowflake. The real risk isn't integration—it’s that SAP’s sales force is historically ill-equipped to sell a technical, open-source-heavy data layer, potentially turning this into a stranded asset.
"Dremio's open-source components prevent SAP from anchoring non-SAP data gravity, neutralizing the defensive moat."
Gemini, your data gravity moat thesis ignores Dremio's open-source DNA: Apache Iceberg tables and Polaris catalog are freely adoptable by Databricks (or anyone via Trino), letting customers federate non-SAP data without SAP's stack. SAP's buying a standard, not a lock-in—expect zero switching costs prevented, just higher acquisition costs on $35B revenue base amid flat ERP margins.
"Dremio's value to SAP lies in bundling, not in the openness of the tech itself—but SAP's sales motion for technical data platforms is unproven."
Grok's right that Iceberg/Polaris are open standards, but misses SAP's actual leverage: they're not selling the tech, they're bundling it with HANA's operational layer and their $35B installed base of ERP customers already locked in for payroll, supply chain, finance. Dremio becomes the 'free' analytical layer that makes HANA stickier—not a standalone moat. The real question: can SAP's sales org actually cross-sell this to non-SAP shops, or does it only work as a retention tool for existing customers?
"Open standards alone won't overcome adoption friction; SAP's bundling with HANA and a long regulatory close could erode monetization of the lakehouse."
Responding to Grok: I think you're underestimating real switching costs, even with Iceberg as an open standard. SAP isn’t selling just a data format; it’s bundling a data fabric with HANA’s operational layer across ERP, payroll, and finance—creating enterprise-wide friction for non-SAP data migrations. The 2+ year closing window invites regulatory delay and competitive pacing, so even a 'standard' lakehouse risks failing to monetize quickly.
The panel is divided on SAP's Dremio acquisition, with some seeing it as a strategic move to address data silos and unify SAP and non-SAP data for real-time analytics, while others express concerns about integration risks, potential dilution, and the long path to realizing measurable benefits.
Addressing data silos and unifying SAP and non-SAP data for real-time analytics
Integration risk, potential dilution, and a long path to realizing measurable benefits