SAP Business Data Cloud + Databricks: A New Era of AI-Powered Insights

In today’s data-driven world, the ability to efficiently manage and analyze vast amounts of information is key to staying competitive. SAP, a leader in enterprise resource planning (ERP), recognizes this need and has developed the SAP Business Data Cloud, a comprehensive solution that unifies and governs SAP data while integrating third-party sources seamlessly.

SAP has teamed up with Databricks, a leader in data analytics and AI, to transform how businesses unlock the full potential of their data. This collaboration combines SAP’s trusted data infrastructure with Databricks’ scalable cloud and AI capabilities, offering businesses a powerful platform to drive innovation and make data-driven decisions faster.  

SAP-Databricks integration transform enterprise data management by re-shaping the future of analytics and AI across industries. 

SAP's Data History: What's Been Good, What's Been Hard 

SAP systems have been at the center of enterprise data for years, holding important information from applications such as ERP, CRM, and SCM. SAP systems are great at transactional processing, generating enormous amounts of potentially useful data. 

 

However, accessing and analyzing SAP data can be difficult. Its complex structures and rigid model make it challenging to run flexible queries that are required for modern analytics. The traditional method to extract large datasets from SAP is slow and costly. 

 

Moreover, SAP’s proprietary integration methods don’t easily connect to third-party systems, limiting the ability to unlock the full value of the data. To integrate SAP data with modern platforms it usually requires custom solutions which raise development and maintenance costs.  

Databricks Enters

Databricks provides a single analytics platform based on Apache Spark, revolutionizing the management of SAP data. Its Lakehouse architecture brings the advantages of data lakes and warehouses together with cost-effective storage and high-performance query capabilities. With cloud infrastructure scalability, Databricks handles big SAP datasets efficiently while fostering interoperability and preventing vendor lock-in. 

The SAP-Databricks partnership simplifies advanced analytics, machine learning, and real-time insights. It helps data professionals accelerate AI models and generate AI on SAP data. Databricks is investing $250M to support customers and partners in leveraging SAP Databricks for deployments and migrations, unlocking SAP data’s full business value. 

Role of SAP and Databricks in Agentic AI  

• SAP has rich experience in mission-critical enterprise applications — finance, procurement, HR, logistics, and so on. Only high-quality semantically rich data is offered by companies like SAP to drive advanced AI applications. Organized and structured data is simpler to integrate with AI systems for enhanced analytics. 

• Databricks offers its Data Intelligence Platform for machine learning, data engineering, and AI workloads. The Unity Catalog offers uniform security and governance across all platforms, now data governance and management are simpler for everyone. To collaborate with a partner like Databricks that is experienced in processing enterprise-scale data and training machine learning models is thus the best solution for SAP in placing customers on the path to AI transformation. 

4 Reasons the SAP + Databricks Integration Matters 

This integration isn’t just a technical upgrade—it’s a fundamental shift in how businesses can harness their business-critical data for AI, analytics, and real-time decision-making. 

1. A Break from the Past: Traditional SAP data access required expensive workarounds (like slow and complex one-directional solutions) and data duplication. Now, data moves securely and efficiently without unnecessary complexity. 

2. A Smarter Path to AI: With all finance, HR, and operational data now available, a complete view of the business can streamline AI and machine learning efforts, making real-time insights more achievable. 

3. Beyond IT: This shift benefits all departments—finance, supply chain, operations, HR, and more—with better, real-time data. The challenge lies in bridging the expertise of in-house data teams, familiar with SAP, to leverage Databricks effectively. 

4. A Stronger Case for Modernization: This integration is built for RISE with SAP and S/4HANA. For companies still on legacy ECC or on-prem SAP, this is another reason to consider the move to the cloud.

Unlocking AI and Advanced Analytics with SAP Business Data Cloud 

SAP has launched its Business Data Cloud (BDC) in partnership with Databricks, marking a significant step toward unifying enterprise data and AI capabilities. This collaboration aims to provide a trusted data foundation, enabling more impactful decisions and fostering reliable AI across the enterprise by natively embedding Databricks technology, the SAP BDC harmonizes data from mission-critical applications with data engineering and business analytics, paving the way for next-level innovation and insights. 

AI-Driven Insights and Predictive Analytics 

The SAP Business Data Cloud unlocks the full potential of enterprise data for business AI by integrating SAP’s expertise in end-to-end processes with Databricks’ data engineering capabilities. This integration facilitates the creation of AI-driven insights and predictive analytics across various lines of business. The platform delivers fully managed SAP data products across all business processes, maintaining original business context and semantics for immediate access to high-quality data without costly extraction processes. 

For example, a CFO can assess the impact of rising inflation on profitability by integrating real-time external data, such as the consumer price index, with financial data products. 

Enabling Cross-Functional AI Applications and Scenario Simulations 

By combining SAP and Databricks, businesses can unlock powerful cross-functional AI applications and run dynamic scenario simulations to drive better decision-making. 

Fig 1: SAP Databricks

Image Source

 

SAP Business Data Cloud, when integrated with Databricks' advanced AI and analytics capabilities, enhances the capabilities of Joule, SAP's generative AI copilot. With Databricks' machine learning models and SAP's data foundation, Joule agents can understand end-to-end business processes and collaborate across different business functions—such as finance, sales, operations, and supply chain—to solve complex challenges in real-time.

 

Additionally, the partnership offers pre-built AI models and scenario simulation tools that are powered by real-time SAP data combined with Databricks' advanced analytics. These tools help organizations model different “what-if” scenarios—such as testing the impact of a supply chain disruption or analyzing how changes in pricing or marketing strategies might affect sales.  

 

With the integration of Databricks and SAP, businesses also have the flexibility to create custom AI agents using the agent builder feature. This enables organizations to tailor the AI models based on their unique data, business context, and cross-functional needs, providing a more personalized approach to solving business problems. 

 

It delivers fully managed SAP data products across all business processes. These curated data products align to a highly optimized and unified “one domain model,” maintaining their original business context and semantics, which means you get immediate access to high-quality data you can trust. 

 

Since these data products are fully managed by SAP, you no longer bear the hidden costs of rebuilding and maintaining data extracts. The SaaS experience simplifies life cycle management, ensures data consistency, and enables zero-copy sharing across your data and analytics ecosystem.

Benefits of Combining SAP Data with Databricks

Combining SAP data with Databricks offers several advantages that can significantly enhance business operations:

Fig 2: Benefits of SAP Data with Databricks 

 

1. Predictive Analytics: Enables companies to predict trends and make informed strategic decisions. Illustration: Real-time analytics enable companies to immediately respond to market trend changes and customer requests as well as operational issues.
2. A Data Vault: Allows you to perform advanced analytics and AI-powered insights with little data engineering effort. This capability enables firms to uncover the underlying patterns and trends in their data, which enable smarter decision-making.
3. Scalability: Built to handle large-scale AI deployments across various cloud environments. With this scalability, AI solutions can scale with the business that is dedicated to delivering better AI workloads efficiently.

What the SAP and Databricks Team-Up Really Means  

For data experts, the SAP Databricks partnership may significantly change workflows. 

Easier Data Mixing

• The new SAP-Databricks partnership aims to simplify this with pre-built connectors using SAP's ODP (Operational Data Provisioning) for efficient data replication.  

• Optimized APIs and simplified pipelines, possibly with visual interfaces in Databricks, will accelerate data ingestion, reduce project timelines and boost productivity for data experts. 

• Connectors will support near real-time data streaming into Databricks, reducing the amount of custom ETL code. The SAP Business Data Cloud native integration and zero-copy data movement also speed up the process, improving team productivity. As an example, combining SAP sales data with web customer data becomes simpler with Databricks

Smarter Analysis and Ideas

• Databricks enhances SAP data analysis by leveraging Spark’s powerful processing capabilities, allowing businesses to run complex queries on large datasets—something traditional SAP tools struggle with. With Databricks dashboards, SAP insights are easily visualized in real time, making data more actionable. 

• Advanced analytics, like machine learning, are now more accessible in Databricks, helping businesses uncover hidden patterns and make faster, better decisions. This improves SAP data analysis and creates new opportunities for process optimization. 

Better Data Control and Safety

• Integrating SAP with Databricks requires strong security measures, including aligning Databricks with SAP’s user authorization to ensure consistent access restrictions across both platforms. Users’ SAP data access limits should be enforced within Databricks. 

• Databricks must also follow SAP’s encryption standards and incorporate features like audit logging and data lineage to ensure data security and compliance. 

Faster Project Work

• Databricks' collaborative notebooks and streamlined workflows can speed up SAP data application development. By improving team productivity and knowledge sharing, pre-built connectors and visual pipeline tools help data experts iterate faster, deploy solutions sooner, and adapt to changing needs.  

Real-time Action

• Databricks streaming facilitates real-time SAP data processing, where companies can process data in real-time for uses such as fraud detection and supply chain monitoring. Real-time pipelines process data, compute KPIs, and send alerts, enabling companies to make fast, data-driven decisions and improve operations with more agility. 

How to Implement AI Agent with SAP Databricks

Implementing AI involves several strategic steps:

Data Preparation and Integration

• Data Unification: Merges SAP and non-SAP data via Unity Catalog and enables customers to develop a single, unified data estate. It is in favor of the idea that all data is available and uniform across the company.
• Data Quality: Maintains high-quality data that can have a significant effect on AI applications by cleaning and transforming as well as validating data. Training AI models demands rich data.

Building AI Models in Databricks

Building AI models in Databricks involves a streamlined process leveraging its scalable infrastructure and integrated tools:

1. Data Preparation: Ingest and preprocess data (e.g., images, videos) with Apache Spark for parallel processing and Delta Lake for strong storage and versioning.
2. Model Building: Use Databricks MLflow to develop models, track experiments, and hyperparameter tuning to offer the best performance for applications like computer vision.
3. Humanize AI Model Training: Train models on GPU-accelerated Databricks clusters with dynamic scaling of compute resources for effective processing of large datasets.
4. Deployment: Deploy models through MLflow to edge devices or cloud platforms (Azure ML, AWS SageMaker) for inference.

Deploying AI Solutions in SAP Environments

• Integration: Binds AI models harmoniously to SAP applications for smooth functioning. Integration enables seamless application of AI-driven insights to business processes directly.
• Monitoring: Ongoing monitors AI performance and updates models as and when required to maintain them as accurate and current. Monitoring makes AI solutions respond to changing business environments and data patterns.

Data Governance Best Practices

• Access Control: Safely grants access to confidential information by using role-based access controls and encryption. This protects information against unauthorized use and data breaches.
• Compliance: Adheres to industry standards of data privacy and security, i.e., GDPR and HIPAA. Compliance is required to make sure that data practices are compliant with regulations.

Ensuring Compliance with Industry Standards

• Regulatory Compliance: Adheres to regulations such as GDPR, HIPAA, and CCPA for data security and privacy. Adhering to these regulations is crucial to build trust and avoid legal conflicts.
• Data Encryption: Protects data in transit and at rest using advanced encryption technologies. Encryption ensures that data is secure even if intercepted or accessed illegally.

Key AI Use Cases with SAP Databricks  

Fig 4: Use Cases with SAP Databricks  

 

1. Predictive Analytics and Forecasting: Uses past data to forecast future trends and results. This feature enables businesses to foresee market changes, regulate inventory, and streamline supply chains better. 

2. AI-Powered Process Automation: Automates repetitive and mundane tasks for greater efficiency and lower operational costs. AI-driven automation can enhance processes like data entry, document processing, and customer support. 

3. Customer Insights and Personalization: Offers customized customer experiences through data-driven insights. Through the analysis of customer behavior and preferences, companies can provide personalized products, services, and marketing campaigns. 

What Services and Tools Do SAP and Databricks Offer? 

Fig 5: SAP and Databricks Integration Services

 

• Deployment Options: SAP Databricks platform deployment models need consideration. Cloud-native deployment on major providers (Azure, AWS, GCP) is likely primary, leveraging Databricks' cloud architecture. Hybrid deployment, for on-premises SAP users, Potential on-premises Databricks extensions, integrating with SAP infrastructure, might emerge.  

• Managed Services: Managed services from SAP or Databricks can simplify integration by handling setup, maintenance, security, and support. Clarification is needed on whether SAP offers managed Databricks instances or vice versa. These services reduce administration, enhance reliability, and can impact cost and efficiency. 

• Support and SLAs: A robust support model and clear SLAs are crucial for enterprise solutions. Support for the SAP-Databricks integration covers both platforms, with clearly defined responsibilities between SAP and Databricks. SLAs specify response times, resolution targets, and uptime guarantees to ensure business continuity. 

• Availability Regions: Organizations operating globally need platform availability in relevant regions, complying with data sovereignty and minimizing latency. Geographic availability of SAP Databricks is key for global use. Also, disaster recovery planning requires geographically diverse availability zones for outage resilience.

• Integration with Existing SAP Landscapes: SAP and Databricks offer seamless integration with existing SAP environments, ensuring compatibility with systems like ECC, BW, and S/4HANA while respecting SAP’s security models. They provide clarity on the impact of current SAP contracts, migration paths to Databricks, and strategies for coexistence during the transition. 

The Future of SAP Data Work: What is Expected 

The collaboration between SAP and Databricks is going to introduce major transformations to SAP data engineering. Some of the most important trends we expect are as follows: 

• Increased Cloud Adoption: More organisations will move their SAP data to the cloud, acknowledging its scalability, flexibility, and cost-effectiveness. 

• Growth in Real-Time Analytics: There will be increased demand for real-time analysis of SAP data, which will drive the usage of streaming pipelines and real-time processing. 

• More AI/ML Integration: More companies will employ AI and ML with SAP data for deeper insights and automation using platforms such as Databricks. 

• Emphasis on Data Governance: Data quality and trustworthiness in hybrid cloud environments will become important, and this will require good governance practices. 

• Evolving Skills for Data Experts: Data professionals will have to evolve by learning skills in cloud data engineering, Spark, and AI/ML to stay ahead of these changes.

   

Final Thoughts: Maximizing SAP Data Potential with Databricks

The SAP and Databricks partnership are an exciting milestone for data experts at SAP, representing an excellent opportunity to make data integration easier, enable insights, and accelerate application development. To achieve these, it's worthwhile to play with the platform, get ready adequately, and stay aware of things that could go wrong. Data professionals must experience Databricks, play with its capabilities, and determine how they can implement them in practice. Upskilling would be imperative because addressing problems and taking the strategic stand will become paramount in making it in the future of SAP data engineering.