Across industries like manufacturing, energy, life sciences, and retail, data drives decisions on durability, resilience, and sustainability. A significant share of this critical data resides in SAP systems, which is why so many business have invested i SAP Datasphere. SAP Datasphere is a comprehensive data service that enables seamless access to mission-critical business data across SAP and non-SAP systems. It acts as a business data fabric, preserving the semantic context, relationships, and logic of SAP data. Datasphere empowers organizations to unify and analyze their enterprise data landscape without the need for complex extraction or rebuilding processes.
No single platform architecture can satisfy all the needs and use cases of large complex enterprises, so SAP partnered with a small handful of companies to enhance and enlarge the scope of their offering. Databricks was selected to deliver bi-directional integration with their Databricks Lakehouse platform. This blog explores the key features of SAP Datasphere and Databricks, their complementary roles in modern data architectures, and the business value they deliver when integrated.
What is SAP Datasphere?
SAP Datasphere is designed to simplify data landscapes by creating a business data fabric. It enables seamless and scalable access to SAP and non-SAP data with its business context, logic, and semantic relationships preserved. Key features of the data fabric include:
- Data Cataloging
Centralized metadata management and lineage. - Semantic Modeling
Retaining relationships, hierarchies, and KPIs for analytics. - Federation and Replication
Choose between connecting or replicating data. - Data Pipelines
Automated, resilient pipelines for SAP and non-SAP sources.
What is Databricks?
A data lakehouse is a unified platform that combines the scalability and flexibility of a data lake with the structure and performance of a data warehouse. It is designed to store all types of data (structured, semi-structured, unstructured) and support diverse workloads, including business intelligence, real-time analytics, machine learning and artificial intelligence.
- Unified Data Storage
Combines the scalability and flexibility of a data lake with the structured capabilities of a data warehouse. - Supports All Data Types
Handles structured, semi-structured, and unstructured data in a single platform. - Performance and Scalability
Optimized for high-performance querying, batch processing, and real-time analytics. - Simplified Architecture
Eliminates the need for separate data lakes and data warehouses, reducing duplication and complexity. - Advanced Analytics and AI
Provides native support for machine learning, predictive analytics, and big data processing. - ACID Compliance
Ensures reliability and consistency for transactional and analytical workloads using features like Delta Lake. - Cost-Effectiveness
Reduces infrastructure and operational costs by consolidating data architectures.
How do they complement each other?
While each architecture has pros and cons, the point of this partnership is that these two architectures are better together. Consider a retail company that combines SAP Datasphere’s enriched sales and inventory data with Databricks Lakehouse’s real-time analytics capabilities. By doing so, they can optimize pricing strategies based on demand forecasts while maintaining a unified view of their data landscape. Data-driven enterprises can achieve the following goals by combining these two architectures.
- Unified Data Access Meets Unified Processing Power
A data fabric excels at connecting data across systems while retaining semantic context. Integrating with a lakehouse allows organizations to bring this connected data into a platform optimized for advanced processing, AI, and analytics, enhancing its usability and scalability. - Advanced Analytics on Connected Data
While a data fabric ensures seamless access to SAP and non-SAP data, a lakehouse enables large-scale processing, machine learning, and real-time insights. This combination allows businesses to derive richer insights from interconnected data, such as predictive modeling or customer 360° analytics. - Data Governance and Security
Data fabrics provide robust governance by maintaining lineage, metadata, and access policies. Integrating with a lakehouse ensures these governance frameworks are applied to advanced analytics and AI workflows, safeguarding compliance while driving innovation. - Simplified Data Architectures
Integrating a fabric with a lakehouse reduces the complexity of data pipelines. Instead of duplicating or rebuilding data in silos, organizations can use a fabric to federate and enrich data and a lakehouse to unify and analyze it in one scalable platform. - Business Context for Data Science
A data lakehouse benefits from the semantic richness provided by the data fabric. Analysts and data scientists working in the lakehouse can access data with preserved hierarchies, relationships, and KPIs, accelerating the development of business-relevant models. Add to that the additional use cases provided by Generative AI are still emerging.
Conclusion
The integration of SAP Datasphere and the Databricks Lakehouse represents a transformative approach to enterprise data management. By uniting the strengths of a business data fabric with the advanced analytics and scalability of a lakehouse architecture, organizations can drive better decisions, foster innovation, and simplify their data landscapes. Whether it’s unifying SAP and non-SAP data, enabling real-time insights, or scaling AI initiatives, this partnership provides a roadmap for the future of data-driven enterprises.
Contact us to learn more about how SAP Datasphere and Databricks Lakehouse working together can help supercharge your enterprise.
