Mastering SAP Datasphere: Unlocking Performance with Decision Trees and Artifact Selection

A critical aspect of working with SAP Datasphere is selecting the appropriate development artifact for your specific needs, whether it's SQL Views, Data Flows, or Transformation Flows. Additionally, leveraging decision trees can complement your data modelling and analytics processes. This blog combines insights on Datasphere performance optimization, decision tree implementation, and artifact selection strategies.

Understanding Development Artifacts in SAP Datasphere

SAP Datasphere provides several development artifacts for enhancing datasets, each suited to specific scenarios.

Below is a guide to selecting the right one:

1. SQL Views

SQL Query: Ideal for straightforward requirements that can be addressed with a single, readable SELECT statement.
SQL Script (Table Function): Best for complex logic that demands advanced scripting capabilities.

Key Considerations:

Persistent data tables generally offer better performance than views.
Choose SQL views for simpler, easily readable transformations.
For SQL view performance issues, consider persisting the results in a table.

2. Data Flows

Data Flows are useful for transforming and loading large datasets into persistent tables, which can significantly improve performance.

Advantages:

Enable complex transformations and loading into persistent tables.

Limitations:

Script Operator Constraints: The script operator cannot access external tables within its code; it only processes its input data.
Performance Issues: Python-based script operators may exhibit slower performance compared to local environments or SQLScript options (40-50 times slower in some cases).
Delta Capture Restrictions: Data Flows cannot target local tables with delta capture capabilities and can only read active data tables from these sources.
Parameter-Driven Views: Views requiring parameters are not usable as Data Flow sources.

3. Transformation Flows

Transformation Flows are designed for complex data transformations that exceed the capabilities of SQL Views but require writing results to persistent tables for better performance.

Key Considerations:

Persistent tables generally outperform views in terms of performance.
Use Transformation Flows when dealing with intricate transformations and the need for persistent data storage.
Ensure that delta capture fields (e.g., Change_Type, Change_Date) in local tables are managed within the flow when data is modified.

When to Use Data Flow, Transformation Flow, or SQL View

Selecting the appropriate artifact depends on your specific use case:

Artifact	Use Case	Key Limitations
SQL View	Simple, readable transformations	Performance issues with complex logic; persistence required for optimization
Data Flow	Large datasets, complex transformations	Limited script operator capabilities; slow Python performance; delta capture restrictions
Transformation Flow	Complex transformations requiring persistent results	Requires manual management of delta capture fields for updates

Decision Tree for Artifact Selection

Below is a “Decision Tree” diagram originally referenced in the blog Datasphere Data Flow, Transformation Flow, SQL View, which I utilize when making decisions on how to optimize performance, transform data, and choose the appropriate artifact for specific use cases.

Example Scenario: Expanding Financial Accounts for Consolidation

Imagine a finance team that manages a global Chart of Accounts across multiple business entities. Their primary objective is to ensure that financial statements are harmonized across entities for consolidated reporting.

However, to generate the detailed-level reporting required by auditors and management, each account range (e.g., 1000–1050) in the source data must be expanded into individual accounts (1000, 1001, 1002, ..., 1050). This process allows the team to track and analyse specific account-level transactions while preserving the original metadata.

To achieve this, SAP Datasphere provides multiple transformation options, such as Data Flows and Transformation Flows with SQL Scripts. While Data Flows are highly effective for simpler, rule-based transformations, they may not be the best fit for certain complex scenarios that require iterative or procedural logic.

Steps in the SQL Transformation Flow:

Filter the Source Data: The transformation selects only the relevant records for the process, ensuring that unnecessary data is excluded.
Expand Account Ranges: For records where the "Account From" and "Account To" fields define a range, the logic iterates over the range, creating a new row for each individual account.
- Example: If Account From = 1000 and Account To = 1003, the output will include rows for 1000, 1001, 1002, and 1003.
Preserve Records Without Ranges: If the "Account From" and "Account To" fields are the same (or the range is invalid), the transformation keeps the record as-is, without modification.
Exclude Certain Records: Rows with a "Financial Statement Item" value of ZZZZZZZZZ are excluded from the expansion process but are still passed to the output for completeness.
Output Expanded Data: The expanded data, along with the original metadata (e.g., FS Version, Chart of Accounts, D, C, Group Balance), is returned in a temporary table for further use.

Here we have written SQL Script (Table Function) to do the above.

Example Input Data:

FS Version	Financial Statement Item	Chart of Accounts	Account From	Account To	D	C	Group Balance
V1	Revenue	CoA_Global	1000	1003	100	200	300
V1	Expense	CoA_Global	2000	2000	50	150	100
V1	ZZZZZZZZZ	CoA_Global	3000	3002	20	10	30

Example Output Data:

FS Version	Financial Statement Item	Chart of Accounts	Account From	Account To	D	C	Group Balance
V1	Revenue	CoA_Global	1000	1000	100	200	300
V1	Revenue	CoA_Global	1001	1001	100	200	300
V1	Revenue	CoA_Global	1002	1002	100	200	300
V1	Revenue	CoA_Global	1003	1003	100	200	300
V1	Expense	CoA_Global	2000	2000	50	150	100
V1	ZZZZZZZZZ	CoA_Global	3000	3002	20	10	30

Conclusion

Optimizing performance in SAP Datasphere requires understanding the strengths and limitations of its development artifacts. Combining these capabilities with decision trees unlocks new opportunities for data-driven insights. By selecting the right tool for the task and leveraging machine learning models, you can maximize the value of your SAP Datasphere implementation.

If you or your colleagues have further questions and would like to understand more about this, please feel free to contact us - services@seaparkconsultancy.com