As enterprises increasingly embrace artificial intelligence and machine learning (ML) to gain competitive advantages, the importance of a robust and well-structured data foundation cannot be overstated. Machine learning models thrive on high-quality, integrated, and timely data, and SAP Data Warehouse Cloud (SAP DWC) offers a modern platform to build such data foundations. This article explores how to build an effective data warehouse in SAP DWC tailored to the requirements of machine learning projects.
SAP DWC combines the power of SAP HANA’s in-memory database with cloud-native flexibility, advanced data modeling, and seamless integration capabilities. It acts as a unified layer that consolidates data from diverse sources, preparing it for machine learning consumption while ensuring governance, security, and scalability.
When building a data warehouse to support ML workflows in SAP DWC, focus on the following aspects:
Machine learning models require diverse data inputs — structured, semi-structured, and sometimes unstructured — from multiple sources such as SAP ERP, IoT sensors, CRM systems, or external datasets. SAP DWC enables:
Features are the attributes that ML models use to learn patterns. SAP DWC supports creating reusable views and data models that act as feature stores by:
SAP DWC’s in-memory HANA database ensures high performance, enabling fast data retrieval critical for iterative model training and tuning. Its elastic cloud architecture scales storage and compute independently, supporting growing datasets.
SAP DWC provides business-friendly modeling tools alongside SQL and scripting options for data engineers and scientists. This collaboration ensures data models meet ML project requirements and that data scientists can easily access and explore datasets.
While SAP DWC prepares the data, the actual ML model training often happens in specialized environments like SAP AI Core, SAP Data Intelligence, or other cloud ML platforms. SAP DWC enables smooth data export or direct integration with these platforms through APIs, data federation, or data sharing capabilities.
Establish connections to relevant SAP and non-SAP systems. Use ingestion pipelines or federation to collect raw data.
Create virtual data models to cleanse, enrich, and harmonize data. Design views optimized for ML feature extraction.
Develop reusable views that encapsulate feature logic — aggregations, flags, derived metrics — essential for ML model input.
Implement data quality checks and governance policies to ensure trustworthy datasets. Use SAP DWC’s monitoring tools for transparency.
Share the curated data securely with ML platforms for training and deployment.
Building a data warehouse in SAP Data Warehouse Cloud tailored for machine learning models is a strategic step toward harnessing the full potential of AI. SAP DWC provides the tools and capabilities needed to integrate, prepare, and manage high-quality data at scale, empowering data scientists to develop robust ML models that drive business innovation. As organizations continue to adopt AI-driven initiatives, SAP DWC stands out as a foundational platform bridging enterprise data and intelligent applications.