The Data Builder in SAP Datasphere is the primary environment for data modeling and transformation activities. Within the Data Builder, users can create a variety of essential objects to build their data landscape. Among the options provided, you can create Intelligent Lookups (A), which are used for fuzzy matching and data cleansing operations to link disparate data sets. You can also create Task Chains (D), which are crucial for orchestrating and automating sequences of data integration and transformation processes, ensuring data pipelines run efficiently. Furthermore, Replication Flows (E) are designed and managed within the Data Builder, allowing you to configure and execute continuous or scheduled data replication from source systems into Datasphere. "Spaces" (B) and "Connections" (C) are typically managed at a higher administrative level within the SAP Datasphere tenant (e.g., in the System or Connection Management areas), not directly within the Data Builder itself, which focuses on data content and logic.

A database user in SAP Datasphere serves specific technical and security-related purposes that are distinct from typical modeling activities within the Data Builder. One primary purpose is to directly access the SAP HANA Cloud database of SAP Datasphere. For advanced scenarios, such as debugging, executing complex SQL scripts directly, or integrating with specialized tools that require direct database connectivity, a dedicated database user is essential. This access bypasses the higher-level Datasphere modeling environment and interacts directly with the underlying SAP HANA Cloud instance. Another crucial purpose is to provide a secure method for data exchange for 3rd party tools. When external applications, reporting tools, or data integration platforms need to consume data from or write data into SAP Datasphere's underlying database, a database user provides the necessary authentication and authorization mechanism. This ensures that data exchange is secure and controlled, adhering to defined permissions. Creating graphical views (B) is done via the Datasphere UI with a Datasphere user, and accessing all schemas (C) would typically require broad administrative privileges, which might be granted to specific database users, but the core purpose is controlled access, not carte blanche.

When modeling master data within an SAP Datasphere graphical view, SAP strongly recommends using the Dimension semantic usage type. Master data, such as customer information, product details, or organizational hierarchies, provides context and descriptive attributes for transactional data. Marking a view as a "Dimension" explicitly signals to downstream consumption tools (like SAP Analytics Cloud) and other Datasphere models that this view contains descriptive attributes that can be used for filtering, grouping, and providing context to analytical queries. This semantic tagging ensures that the data is interpreted and utilized correctly in analytical scenarios, distinguishing it from "Fact" data (which represents transactional measures) or "Relational Dataset" (a more generic type without specific analytical semantics). Using the "Dimension" semantic usage type aligns with best practices for building robust and understandable data models for analytics.