To write data from a local table in SAP Datasphere to an outbound target, you primarily use a Data Flow. A Data Flow in SAP Datasphere is a powerful tool designed for comprehensive data integration and transformation. It allows you to extract data from various sources (including local tables within Datasphere), perform various transformations (like joins, aggregations, filtering, scripting), and then load the processed data into a specified target. This target can be another local table, a remote table, or an outbound target like an external database or a file system. While a Replication Flow (C) is used for ingesting data into Datasphere, and a Transformation Flow (A) is not a standalone artifact for outbound writes (often part of a Data Flow), the Data Flow provides the complete framework for extracting, transforming, and loading data, including sending it to external destinations.

To load data into SAP Datasphere using the Data Provisioning Agent (DP Agent), two crucial prerequisites must be met. Firstly, the DP Agent must be installed and configured on a local host (A). The DP Agent acts as a bridge between your on-premise data sources and SAP Datasphere in the cloud. It needs to be deployed on a server within your network that has access to the source systems you wish to connect. Secondly, the relevant data provisioning adapter must be installed (B) within the DP Agent framework. Adapters are specific software components that enable the DP Agent to connect to different types of source systems (e.g., SAP HANA, Oracle, Microsoft SQL Server, filesystems). Without the correct adapter, the DP Agent cannot communicate with and extract data from your chosen source. While the Cloud Connector (C) is often used for secure access to SAP backend systems in the cloud, it's not a direct prerequisite for the DP Agent itself for all data sources. Configuring the DP Agent for a specific space (D) is a step after the initial installation and adapter setup.

In an SAP Analytics Cloud (SAC) analytic data model, you typically have a degree of flexibility in managing dimensions. Among the automatically created dimension types, the Generic dimension can often be deleted if it's not relevant or desired for your analysis. Generic dimensions are often generated by the system based on identified data patterns but might not always align with specific business requirements or be redundant. In contrast, Date, Version, and Organization dimensions are fundamental and often system-critical, especially for planning models (Version, Organization) or time-based analysis (Date). These core dimensions are usually not freely deletable or are required by the system for specific functionalities. Therefore, for tailoring your analytic model to specific business needs, the ability to remove generic dimensions provides greater control and simplification.