Mastering Data Transformation in SAP Datasphere: A Comprehensive Guide

Data transformation is a crucial part of any data management process, especially when working with complex systems like SAP Datasphere. Whether you’re integrating data from SAP S/4HANA, SAP BW Bridge, or various remote sources, understanding how to effectively create and manage transformations can unlock the full potential of your data. In this guide, we will explore the various aspects of data transformation in SAP Datasphere, including graphical transformation, SQL View transformation (using both SQL Standard Query and SQLScript), and key considerations such as delta changes and limitations.

Creating a Graphical View Transform

Graphical View Transforms in SAP Datasphere provide a user-friendly interface for building complex data transformations without writing code. You can drag and drop sources, perform joins, unions, and aggregations, and apply filters—all through a visual editor.

Key Steps to Create a Graphical View Transform:

1. Adding a Source: You can add data sources from the Repository or the Sources tab, including Open SQL schemas and remote tables. After selecting your sources, you can join, union, or filter data as needed.
2. Applying Operations:
3. Preview and Validation: Use the preview function to review the transformed data and ensure the transformation logic is correct.

Creating an SQL View Transform: SQL vs. SQLScript

Creating an SQL View Transform allows for more advanced data manipulation using SQL statements. SQL View Transforms can be created using either Standard SQL Queries or SQLScript (Table Functions).

1. Standard SQL Query:

• Standard SQL Query is useful for simpler transformations that involve filtering, joining, and basic calculations.
• It supports a straightforward query structure, making it easier to use for less complex data transformations.

2. SQLScript (Table Functions):

• SQLScript is ideal for more complex logic, enabling you to encapsulate logic in reusable functions.
• This approach offers enhanced performance for heavy data operations by utilizing in-memory processing.
• It supports advanced functionality like loops and conditional processing, making it more versatile than Standard SQL.

Limitations:

• SQLScript objects should only be consumed within SAP Datasphere and not exposed externally.
• Data access controls can impact how SQL views interact with remote tables, particularly if these tables are referenced indirectly.

Handling Delta Changes in Data Loading

Delta loading is a critical feature for efficiently managing large datasets, allowing you to load only the changes since the last load rather than the entire dataset.

Key Considerations for Delta Loading:

• Ensure that the delta capture setting is enabled for both the source and target tables.
• Delta changes include both additions and deletions, which are tracked through specific columns like Change Date and Change Type.
• For remote tables located in SAP BW bridge spaces, additional input parameters like REQTSN_LOW and REQTSN_HIGH are used to manage the delta extraction process.

Additional Information About Remote Tables:

• Remote tables must be shared with the SAP Datasphere space to be used in a transformation flow.
• It is not possible to use an Aggregation node if the delta capture setting is enabled for the source table.
• Delta changes in remote tables are managed through system-defined parameters and settings, ensuring accurate data synchronization.

Creating a Transformation Flow

A transformation flow in SAP Datasphere is used to apply a sequence of operations to load and transform data from various sources into a target table.

Procedure:

1. Initiate a New Transformation Flow: In the Data Builder, create a new transformation flow, selecting whether to use a graphical or SQL view transform.
2. Define Your Transformations: Apply graphical or SQL transformations as needed to shape your data. Use the side panel to configure properties, including deployment status and load type.
3. Deploy and Monitor: After configuring the transformation, save and deploy it to make it operational. Use the Data Integration Monitor to track the status of the transformation runs.

Important Notes:

• Transformation flows are designed to support only local tables within the SAP Datasphere environment.
• The deployment and modification of objects in transformation flows are restricted based on user roles and permissions.

Conclusion

Mastering data transformations in SAP Datasphere involves understanding the tools and methodologies available to create both graphical and SQL view transformations. By leveraging these tools effectively, you can ensure efficient data management, seamless integration, and optimal performance across your data workflows. Whether you're dealing with real-time data replication or complex SQL logic, the ability to adapt and refine your transformation processes is key to maximizing the potential of SAP Datasphere in your data strategy.