- AI, SAP Business Data Cloud (BDC)
- SAP Business AI
- 6 min reading time
Maximilian Hahn
This wiki explores the integration of Artificial Intelligence (AI) and Business Intelligence (BI) for modern reporting in the Databricks Lakehouse. It explains how this combination can help organizations use their data more efficiently and make more informed decisions.
Table of contents
- 1. The Convergence of Two Worlds: The End of Data Silos
- 2. The Foundation: The Databricks Lakehouse Platform
- 3. Modern BI: High-Performance Analytics
- 4. Artificial Intelligence: From Vision to Productive Value Creation
- 5. The Key Factor: Context for the AI Engine
- 6. The Best of Both Worlds: The Synergy Between SAP and Databricks
- 7. Conclusion: A strategic investment in the future
1. The Convergence of Two Worlds: The End of Data Silos
In traditional IT architecture, Business Intelligence (BI) and Artificial Intelligence (AI) were often trapped in technological and organizational silos. While the BI department maintained historically grown data warehouses to generate retrospective analyses, data scientists worked on experimental predictive models in isolated data lakes—like two crews in separate control centers working on the same mission but not with the same tools.
This separation inevitably leads to:
- Inconsistent Truths: Different Logics in the Data Warehouse and the Data Lake.
- High latency: Data must be laboriously moved back and forth between systems (ETL overhead).
AI & BI with Databricks bridges this gap and brings our teams out of their respective control centers and into a shared cockpit—the Lakehouse—for the first time.
2. The Foundation: The Databricks Lakehouse Platform
The Lakehouse concept addresses the shortcomings of traditional architectures. It combines the strict governance and performance features of a data warehouse with the boundless flexibility of a data lake —essentially a combination of a reliable launch vehicle and a flexibly expandable orbital laboratory.
The technological pillars
- Cloud Data Lake as a storage layer: the physical foundation of the lakehouse. Here, all data—whether structured (tables), semi-structured (JSON/XML), or unstructured (images, PDFs)—is stored in cost-effective cloud storage solutions such as Azure Data Lake Storage (ADLS), AWS S3, or Google Cloud Storage. Unlike traditional silos, the Cloud Data Lake enables the storage of massive amounts of data in raw format without having to enforce a rigid structure in advance.
- Delta Lake: This layer brings ACID transactions (Atomicity, Consistency, Isolation, Durability) to the data lake. It ensures that data operations are reliable, enables time travel through data versioning, and forms the basis for the “single source of truth.” Delta Lake thus ensures that the data foundation remains stable—regardless of how many “mission updates” (data changes) occur in the background.
- Unity Catalog: The brain of the platform. As the central governance hub, it manages permissions, lineage (data provenance), and access controls across all assets—whether tables, files, or ML models. This allows analysts and data scientists to work with identical, secure datasets. Unity Catalog acts as a kind of “Mission Control,” precisely tracking who accesses which systems and how data flows through the platform.
3. Modern BI: High-Performance Analytics
Business Intelligence on Databricks today means far more than simply viewing static dashboards. Traditional reports are often nothing more than a snapshot: a crystal-clear view through the rearview mirror. BI on Databricks replaces this view with the panoramic windshield of a cockpit. It enables interactive, scalable analyses based on live data streams. Instead of merely taking stock of the past, you can dynamically respond to events unfolding right before your eyes—and thus proactively set the course.
Maximum performance thanks to the Photon Engine & Spark
Databricks employs a dual-pronged strategy for data processing and can easily handle even high workloads:
- Apache Spark: The proven framework for orchestrating and distributing massive workloads across clusters.
- Photon Engine: A native vectorized execution engine written in C++. Photon is optimized for modern CPU architectures and handles the actual computational work of the queries. The result: SQL queries are processed up to 10 times faster than in conventional systems.
Open Connectivity & Real-Time
Thanks to open standards such as Delta Sharing and robust interfaces (ODBC/JDBC), established tools like Power BI, Tableau, and SAP Analytics Cloud (SAC) can be integrated without any loss of performance. With the native integration of streaming data, reporting shifts from a retrospective view to real-time insights.
4. Artificial Intelligence: From Vision to Productive Value Creation
While BI explains what has happened, AI provides the answer to the question: What will happen? Databricks democratizes this process by minimizing the gap between raw data and intelligent predictions. To pave the way from strategic ideas to operational applications, the platform relies on three technological pillars that cover the entire AI lifecycle:
- End-to-End ML Lifecycle: With MLflow, Databricks integrates the entire model lifecycle—from experimentation in notebooks (Python, R, SQL) through automated training via AutoML to deployment.
- Generative AI & LLMs: Companies can safely train or fine-tune modern large language models on proprietary data within their own cloud environment without losing data sovereignty.
- Data Intelligence: Integrated AI assistants (such as Databricks Assistant or Genie ( see Fig. 2)) lower the barrier to entry for business users. A simple prompt in natural language is all it takes to generate complex SQL queries or create data visualizations.
The following figures use weather data to illustrate how Genie-AI generates and visualizes output based on a natural language prompt.
The following figures use weather data to illustrate how Genie-AI generates and visualizes output based on a natural language prompt.
5. The Key Factor: Context for the AI Engine
A large language model (LLM) without specific context is like a brilliant pilot who hasn’t yet been trained on a specific rocket model. Ability alone isn’t enough.
To deliver accurate, business-relevant results, Databricks relies on three levels of data enrichment for AI:
- Metadata: The AI understands the structure of your tables (e.g., from SAP systems) and interprets fields in the correct context.
- Industry expertise: Industry-specific standards such as ISA-95 for the manufacturing industry can be stored as “instructions” (see Fig. 5). This allows the AI to automatically calculate key performance indicators (e.g., OEE) in compliance with the standards.
- Business objectives: By integrating planned data, the platform enables automated variance analysis and proactive recommendations for action.
6. The Best of Both Worlds:
The Synergy of SAP and Databricks
The key advantage lies in contextualization via a shared semantic layer: In the Lakehouse, data from SAP S/4HANA or BW/4HANA can be seamlessly correlated with external sources such as IoT sensors or market data. The semantic layer acts as a “translator,” converting complex SAP data structures into a unified, business-oriented logic. This integration transforms static transaction data into a dynamic basis for decision-making that goes far beyond traditional SAP reporting and opens up entirely new possibilities for AI-powered forecasting.
Case Study: Intelligent Supply Chain
A manufacturing company monitors daily sales figures from SAP via BI on Databricks. Thanks to the semantic layer, the AI team has direct access to validated metrics to generate accurate demand forecasts. These results are automatically fed back into the SAP system to optimize inventory management. The result: reduced capital tied up in inventory while maintaining maximum delivery capacity through a seamless integration of operational efficiency and AI.
- SAP Databricks (via SAP BTP): Here, Databricks functions as an integrated engine within the SAP Business Technology Platform. This provides deep process integration and leverages existing SAP governance structures—ideal for an “SAP-first” strategy.
- Native Databricks: The platform runs directly on Azure, AWS, or GCP. This approach offers maximum technological freedom, faster access to the latest innovations (e.g., GenAI), and more flexible scaling. It is the top choice when the Lakehouse is intended to serve as a universal data platform beyond the SAP ecosystem.
7. Conclusion: A strategic investment in the future
Consolidating AI and BI on Databricks doesn’t mean replacing your trusted team. It’s about finally giving them a modern, smart dashboard where they can reach their full potential. Instead of laboriously setting up new systems, the Lakehouse integrates seamlessly and maximizes the value of your existing data.
This approach reduces complexity, ensures data sovereignty, and speeds up the decision-making process—while fully protecting your existing investments.
Would you like to fully unlock the potential of your (SAP) data? Our experts will guide you on your journey to data intelligence.
Additional Resources
- Comparison: SAP Databricks vs. Native Databricks
- White Paper: The Technical Foundations of Databricks and SAP BDC
- Download: E-book: Databricks Guide for SAP Customers
Data formats in Databricks: A guide to Parquet, Delta Lake, and alternatives
Choosing the right data format is a critical but often underestimated factor for performance and efficiency in Databricks. The wrong choice can slow down queries and drive up storage costs. But what is the difference between a file format like Parquet and a technology like Delta Lake? This wiki offers a practical guide through the jungle of data formats. It explains why Parquet is the first choice for most analyses, how Delta Lake brings the reliability of a data warehouse to the data lake, and what role alternatives such as Iceberg and Hudi play.
SAP Data to Databricks: A Strategic Guide to Data Integration
How does this work in data sharing with SAP and Databricks? The strategic partnership between SAP and Databricks enables seamless integration. You can provide data from the SAP system directly to Databricks as "business data products" using the "BDC Connector." Thanks to Delta Sharing, this data can then be used "live" and "zero-copy" for analysis and AI applications without having to extract and copy it from the SAP environment.
How does this work in data sharing with SAP and Databricks? The strategic partnership between SAP and Databricks enables seamless integration. You can provide data from the SAP system directly to Databricks as "business data products" using the "BDC Connector." Thanks to Delta Sharing, this data can then be used "live" and "zero-copy" for analysis and AI applications without having to extract and copy it from the SAP environment.
