What are the main data transformation approaches available in Salesforce integrations?
Suggested answer
1. Apex: Full programmatic control. Use for complex transformations, conditional logic, and when transformation is tightly coupled with Salesforce business logic. Most flexible but requires code maintenance and consumes Apex governor limits.
2. DataWeave (MuleSoft): A functional language specifically designed for data transformation. Supports JSON, XML, CSV, Java, and more. Powerful pattern matching and type-safe transformations. Best for middleware-layer transformations when MuleSoft is in the architecture.
3. Flow (Data Transformation elements): Declarative transformations within Flow Builder — field mapping, data type conversion. Suitable for simple field-to-field mapping without middleware.
4. External ETL tools (Informatica, Talend, dbt): Dedicated transformation engines for heavy data processing before or after Salesforce. Best for complex multi-system data reconciliation.
Architectural principle: Keep transformation close to where the data is produced or consumed (canonical data model approach). Avoid repeated transformation across multiple hops, which creates maintenance overhead.
Practice content for interview preparation; not an official vendor answer. Verify details against current product documentation.
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