Map intentionally
Source-to-target mappings that are explicit, testable and aligned to the business meaning of each field.
Turning payload differences intoclear contracts.
DataWeave 2.0 enables readable transformations between system payloads, canonical business models and target-specific API formats.
Transform safely
Account for types, optional fields, errors and target constraints rather than opaque pass-through.
Keep it maintainable
Pair transformations with clear contracts, examples and tests so future changes stay understandable.
DataWeave 2.0 is the MuleSoft transformation language, and most integration bugs live in the mappings. Prince writes DataWeave that is explicit about types, nulls and defaults, maps into canonical models, and keeps system-specific quirks at the edges.
Typical work includes JSON, XML and CSV conversions, Salesforce sObject mapping, flattening and grouping nested payloads, EDI/B2B data, and reusable DataWeave modules shared across applications.
Each non-trivial transformation is paired with sample payloads and MUnit tests so behaviour is documented and regression-safe.
Frequently asked
What is DataWeave used for?
DataWeave transforms data between formats and structures inside Mule applications, for example converting a Shopify JSON order into a Salesforce or SAP payload.
How do you keep DataWeave maintainable?
Use canonical models, small reusable functions and modules, explicit null and default handling, sample-driven tests and comments where business rules are not obvious.