Databricks and SAP Join Forces to Ready Data for AI with SAP Databricks

Enterprise data analytics just got a significant boost. Currently serving 10,000 customers, Databricks, the fast-growing data and AI company that just secured $15 billion in Series J financing at a company valuation of $62 billion, has announced a strategic partnership with SAP to launch SAP Databricks, a native integration of Databricks’ Data Intelligence Platform within SAP’s newly unveiled Business Data Cloud. The collaboration aims to break down data silos, making enterprise data more accessible, AI-ready, and operationally transformative.

Enterprise software has long struggled with fragmented data. SAP powers mission-critical applications across procurement, finance, HR, and logistics, yet integrating SAP data with other business systems has been notoriously complex. SAP Databricks is designed to change that, enabling seamless access to SAP and non-SAP data, all governed under Databricks’ Unity Catalog for security and compliance.

“Every organization is searching for a faster, more reliable way to translate their data into strategic advantage,” said Ali Ghodsi, Co-founder and CEO of Databricks. “Together with SAP, we’re helping businesses seamlessly unify their data sources, streamline analytics, and accelerate the development of domain-specific AI applications.”

SAP Databricks is available as part of SAP Business Data Cloud and will roll out in phases on AWS, Microsoft Azure, and Google Cloud. SAP sees this partnership as a key milestone in data-driven innovation. Muhammad Alam, SAP Executive Board Member, emphasized the transformative potential: “Our partnership with Databricks represents a turning point in how enterprise data is harnessed. We’re fusing SAP’s expertise in mission-critical applications with Databricks’ leadership in AI and data engineering.”

At the heart of SAP Databricks is the ability to combine SAP ERP data with operational and AI-driven insights. Key features include:

• Bi-directional data sharing via Delta Sharing, allowing enterprises to merge SAP and non-SAP data effortlessly.
• A unified data estate governed by Unity Catalog, ensuring consistent security and compliance across all platforms.
• Enhanced AI capabilities powered by Mosaic AI, allowing businesses to develop domain-specific AI applications trained on private SAP data.
• SQL analytics at scale, making SAP data available for advanced reporting, modeling, and AI-driven insights without complex data engineering work.

This streamlined approach removes the traditional barriers that have limited enterprises from extracting full value from their data.

As part of its aggressive push, Databricks has committed $250 million to accelerating customer adoption of SAP Databricks. This investment will help enterprises and system integrator partners deploy and migrate their SAP data more efficiently, ensuring that businesses can leverage the full power of their data assets.

The tech and consulting industries have taken notice, with some of the biggest names in the field praising the partnership:

• accenture’s Karthik Narain, Group Chief Executive – Technology & CTO: “Generative AI is a catalyst for reinvention across the enterprise, but to build and scale AI applications effectively, organizations need to have a complete understanding of their data.”
• Capgemini’s Niraj Parihar, CEO of Insights and Data Global Business Line: “A strong data foundation remains the cornerstone of all successful AI integrations. SAP Databricks will enable clients to seamlessly merge ERP data with operational insights to maximize AI’s value.”
• Deloitte’s Jessica Kosmowski, Global Ecosystems & Alliances Leader: “Breaking down silos between structured and unstructured data is crucial to unlocking true value from AI investments, especially amid constant industry disruption.”
• EY’s Hugh Burgin, EY-Databricks Alliance Leader: “We are excited to leverage the combined strengths of SAP and Databricks to transform data into trusted business insights.”

SAP Databricks is more than just another data integration tool—it’s a gateway to the next generation of enterprise AI. With businesses increasingly turning to AI for predictive analytics, automation, and personalized customer experiences, SAP Databricks offers a foundation for organizations to build intelligent, data-driven applications at scale.

The partnership aligns with the broader industry trend toward data unification, AI democratization, and cloud-driven digital transformation. As AI becomes a core driver of business value, solutions like SAP Databricks will help enterprises move from reactive data management to proactive, AI-powered decision-making.

What this means for SAPinsiders

SAP Databricks scratches an itch worth scratching. Integrating SAP data with non-SAP systems has long posed significant challenges for organizations, often hindering digital transformation efforts. A survey revealed that 89% of digital transformation projects stall due to SAP integration issues. Additionally, 51% of ERP professionals identify data integration challenges as a primary obstacle in leveraging ERP data to achieve business objectives. These challenges stem from several factors:

• Complexity of SAP’s Proprietary Structures: SAP’s unique data formats and customized modules complicate integration with external systems.
• Distributed Data Landscapes: Organizations often manage data across various environments, including on-premises and cloud platforms, making seamless integration difficult.
• Resource Constraints: A lack of skilled personnel to develop and maintain data pipelines further exacerbates integration difficulties.

Partnership should help shore up the critical foundation for AI to go mainstream. The effectiveness of AI systems is fundamentally tied to the quality of data they process. High-quality, current, and accurate enterprise data is essential for maximizing AI’s value, as it directly influences the performance, reliability, and trustworthiness of AI models. Studies have consistently shown that poor data quality can significantly impair AI functionality. Inaccurate, incomplete, or inconsistent data can lead to unreliable predictions, biased results, and flawed decision-making. For instance, a 2023 study indicated that 31% of surveyed companies identified poor data quality as a barrier to leveraging AI effectively. Moreover, research highlights that AI systems trained on incomplete or biased data can produce inaccurate outcomes, potentially infringing on individuals’ fundamental rights and leading to discriminatory practices. Enterprises often face significant challenges in maintaining high-quality data, which in turn affects AI outcomes. Common issues include:

• Data Silos: Isolated data systems prevent the integration of comprehensive datasets necessary for AI analysis.
• Inconsistent Data Standards: Variations in data formats and definitions can lead to misinterpretations and errors in AI processing.
• Manual Data Entry Errors: Human errors during data entry can introduce inaccuracies that skew AI model training and predictions.

Watch for early adopters of SAP Databricks to work out any kinks. Early adoption is anticipated primarily among large enterprises with complex data ecosystems that rely heavily on SAP for critical business operations. Industries such as manufacturing, finance, and supply chain management, where SAP’s presence is prominent, are likely to lead in embracing SAP Databricks. Companies aiming to enhance their AI capabilities by integrating SAP data with diverse data sources will find this solution particularly advantageous. Specific applications expected to drive early adoption include:

• Agent Systems: Developing AI agents capable of automating complex tasks by leveraging integrated data.
• Demand Forecasting: Enhancing predictive analytics to optimize inventory and meet market demands effectively.
• Predictive Maintenance: Utilizing integrated data to anticipate equipment failures and schedule timely maintenance, reducing downtime.
• Customer Segmentation: Refining marketing strategies through detailed analysis of customer data.
• Supply Chain Optimization: Streamlining operations by providing comprehensive insights into supply chain dynamics.