Outlining Modeling Integration Options

Objective

After completing this lesson, you will be able to describe modeling integration options in SAP Datasphere.

Modeling Integration

Besides creating objects in SAP Datasphere using the Data Builder, there are also possibilities to import objects using Semantic Onboarding, exchange objects with HDI containers or use Open SQL Schema. Let's explore these modeling integration options.

Semantic Onboarding

The figure shows a screenshot of Semantic Onboarding (SAP Systems, Content Network and Data Products).

Semantic Onboarding offers a central entry point for importing semantically-rich objects from your SAP systems and the Content Network, as well as the Public Data Marketplace and other marketplace contexts.

The figure shows screenshots of the Import Entities wizard for metadata from SAP S/4HANA , SAP BW/4HANA and SAP BW Bridge.

You can use the Import Entities wizard to import semantically-rich objects from selected SAP systems. The wizard creates Business Builder and Data Builder entities (along with all the objects on which they depend) in SAP Datasphere.

You can import objects from the following SAP systems:

  • SAP S/4HANA Cloud
  • SAP S/4HANA on-premise
  • SAP BW/4HANA
  • SAP Datasphere, SAP BW Bridge
  • SAP HANA Cloud

When importing from SAP S/4HANA systems, use the Import Entities wizard for CDS views wherever possible. It leverages rich semantic metadata to import higher-level objects and automatically follows associations to dimensions, hierarchies, and text entities, including them in the import.

Association following is supported for SAP S/4HANA Cloud connections and for SAP S/4HANA on-premise connections running SAP_BASIS 756 or higher with data access configured as 'Replication Flow to Local Tables'. For earlier releases, association information is embedded in the CSN definition and automatically reconstructed when the target entities are imported later.

HDI Containers

The figure shows how data of HDI Containers and Spaces can be exchanged in modeling scenarios as described below this picture.

It is possible to enable SAP SQL data warehousing on your SAP Datasphere tenant to exchange data between your HDI containers and your SAP Datasphere spaces without the need for data movement.

This integrated SAP SQL data warehousing approach allows you to add HDI containers to your space and exchange data between them:

  1. Use calculation views and other SAP Business Application Studio objects as sources for your SAP Datasphere local tables and data flows.

  2. Use SAP Datasphere views that are exposed for consumption as sources for your SAP Business Application Studio calculation views and flowgraphs.

  3. Use SAP Business Application Studio tables as targets for your SAP Datasphere data flows.

Note

For detailed configuration of these three scenarios, you can find more information here:

https://help.sap.com/docs/SUPPORT_CONTENT/datasphere/5551410700.html?locale=en-US&version=LATEST

In SAP Datasphere, you can also use SAP HANA calculation views, generated from SAP BW/4HANA objects, which are located in schema _SYS_BIC.

Open SQL Schema

The figure shows how external tools can be used with SAP Datasphere using Open SQL Schema.

An Open SQL Schema in SAP Datasphere provides a flexible SQL endpoint that enables secure data integration and modeling capabilities through database users. It creates a dedicated schema in the underlying SAP HANA database that allows both read and write operations, facilitating seamless data exchange between SAP Datasphere and external systems.

Here are some examples how the Open SQL Schema can be used:

  • Create tables, views, and stored procedures using DDL and DML statements.
  • Write data to tables.
  • Create a table to act as a target to receive data written from a data flow.
  • Serve as a bridge for data extracted from third-party systems via middleware.
  • Leverage SAP HANA's embedded machine learning capabilities including APL, PAL, spatial engine, text processing, and graph algorithms for advanced analytics and predictive modeling.
  • Leverage data anonymization and data masking.
  • Data science: use tooling like Jupyter notebooks.