Data Mapping Best Practices

Table of Content

Objective:

This document provides a comprehensive overview of best practices in data mapping as outlined in the provided file. If you need further details or have any specific requests, please let me know!

Data Mapping Best Practices:

1. Naming Conventions

• Activity Naming Syntax: Use a standardized naming convention for data mapping activities. For example: [Project/Organization][ActivityType][Transaction][ActivityName].

  • Example: FIL_DM_INBOUND_CashConversion where FIL is the project acronym and DM stands for data mapping.

2. Guidelines for Data Mapping

• Purpose Description: Clearly describe the purpose of data mapping in the mapping description.

• User Role in Development: Ensure development activities are done by an IT user, not an admin user.

• Conversion Logic: Use Value Map for data conversion instead of When conditions whenever possible.

• Simplicity: Write descriptions in simple and easy-to-understand language.

Open

3. Working with Variables

• Variable Naming: Name variables clearly to describe their purpose, beginning each name with "var" followed by capitalized words (e.g., varAcceptanceCriteria).

• Variable Declaration: Declare variables at the appropriate node. If a variable is needed on multiple nodes, declare it on the parent node.

• Avoid Redundancy: Do not create redundant variables. Use a single variable instead of multiple ones with the same value.

• Optimize Variable Use: Avoid unnecessary context variables, use the same connection variable instead of new connections per record, and remove irrelevant variables when copying mappings.

4. XML Lookups

• Performance Optimization: Prefer XML lookups over database (DB) lookups for better performance by pre-fetching DB records into XML.

• Conditional Lookup: Use lookup within When conditions (if present) to avoid unnecessary lookups in the source XML.

5. XSL (Extensible Stylesheet Language) Usage

• Template Creation: Create XSL templates for logic that needs to be applied across multiple mappings.

• Avoid Unnecessary Custom XSL: Use custom XSL only when no other method can implement the required logic.

• Commenting in XSL: Write comments in custom XSL code to make it understandable for others.

Open

6. Efficient Mapping Techniques

• Mapping Rules Verification: Apply efficient mapping rules and verify them using a debugger.

• Whitespace Management: Use normalize-space only when necessary to optimize performance.

• Filter Empty Elements: Use the option to filter unmapped or empty elements to reduce output size and increase performance.

7. Performance Upgrading

• Splitting and Parallel Processing: For large data volumes, use splitting and parallel processing within the mapping activities.

• Avoid Unnecessary Processing: Avoid using normalize-space excessively as it can increase processing time.

8. DB Query Caching

• DB Query Caching: Use caching for frequently executed queries to improve performance.

• Optimized Data Fetching: For smaller data sets (e.g., less than 5,000 records), create a local variable on the root element and fetch all data into that variable.

9. Data Viewer & Debugger Best Practices

• File Size Limit: Ensure that the file size for testing in the attached dataset does not exceed 100 KB.

• Minimal Test Case: Start debugging with a minimal test case to simplify and isolate the issue.

10. Custom Methods

• Single Responsibility Principle: Each custom method should have a single responsibility and perform a specific task for better code readability and maintenance.

• Descriptive Method Names: Use clear, descriptive names for custom methods that accurately represent their purpose.

11. Mapping Expressions

• Analyze Requirements: Understand the relationships between source and target elements to guide the mapping implementation.

• Encapsulate Complex Logic: Use mapping expressions to handle complex scenarios effectively and ensure they are thoroughly tested and validated.

12. Commenting in Mappings

• Explain Intent, Not Implementation: Comments should focus on the reason behind a mapping rule rather than repeating what the rule does.

• Self-Explanatory Comments: Comments should be concise, clear, and to the point.

13. Custom Auto Map

• Understand Layouts: Clearly understand the source and target layout hierarchy, identifying properties or fields to be mapped.

• Data Validation and Error Handling: Validate and sanitize input data before performing auto-mapping to ensure data integrity.

14. Use Cases

• Use Case I: Filtering records based on DB queries.

  • Solution: Define a local variable at the root level and use it within conditional rules to achieve the desired output.

• Use Case II: Improving performance for large data sets.

  • Solution: Enable data splitting, switch to the latest XSL transformer (e.g., SAXON), enable DBQuery caching, and optimize DB queries to reduce execution time.

Conclusion

By following these best practices, you can optimize data mapping processes, improve performance, and maintain clear, efficient, and scalable mapping logic.