# Data Transformation
While **Data Mapping** focuses on linking inbound and outbound parameters between components, **Data Transformation** extends this capability by allowing data to be **modified, calculated, or reshaped** during the mapping process.
In ONEWEB, data transformation can be applied **directly on the mapping connection**, enabling developers to adjust values, apply formulas, or construct expressions **before data is passed to the target component**.
This approach keeps transformation logic **close to the data flow** and avoids unnecessary custom code for simple conversions.
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### Data Transformation vs Data Mapping
* **Data Mapping** defines *where* data flows
* **Data Transformation** defines *how* data is modified while flowing
Data Transformation is used when a value **cannot be passed directly** and must be adapted to meet the target contract, such as format differences, calculation requirements, or structural mismatch between systems.
***
### Accessing the Formula Editor
Data Transformation in ONEWEB is implemented through the **Formula Editor**, which is accessed directly from the mapping interface.
To apply transformation logic:
* A mapping is created between a source parameter and a target parameter
* The mapping connection (blue line) is edited
* The **Formula Editor panel** opens
The Formula Editor ensures that transformation logic remains **contextual and traceable**, directly associated with the mapped data.
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### Formula Editor Capabilities
The **Formula Editor** allows developers to construct transformation expressions visually, including:
* Dragging process or Microflow parameters into expressions
* Applying mathematical operators
* Using string manipulation functions
* Combining multiple inputs into a single output expression
Transformation logic defined in the Formula Editor is evaluated during Microflow execution and applied before values reach the target parameter.
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### Common Data Transformation Scenarios
Data Transformation is commonly applied in scenarios such as:
* Formatting string values (e.g. concatenation, trimming, case conversion)
* Applying arithmetic calculations
* Constructing composite values from multiple parameters
* Converting data shapes between heterogeneous systems
* Normalizing values before persistence or service invocation
These transformations enable systems with **different data contracts** to interoperate cleanly and predictably.
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### Best Practices for Data Transformation
To maintain clarity and reliability in Microflow design:
* Keep transformation expressions **simple and readable**
* Avoid embedding complex business rules in formulas
* Use **Microflows or Java Nodes** for advanced or multi‑step transformations
* Use meaningful parameter names to reflect transformed intent
* Validate transformation outputs during testing
Well‑designed transformations improve integration robustness while keeping Microflows easy to understand and maintain.
***
### Summary
**Data Transformation** in ONEWEB allows values to be modified inline during parameter mapping using a **visual, formula‑based approach**.
With Data Transformation, developers can:
* Apply calculations and string operations during mapping
* Adapt data between different system contracts
* Keep transformation logic close to data flow
* Reduce reliance on custom code for simple conversions
Together, **Data Mapping** and **Data Transformation** form a complete visual integration model, enabling flexible, readable, and maintainable data flow within **Microflow Designer** and across ONEWEB applications.
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