Data Transformation Between Systems
When integrating different software systems, the data formats, field names, value representations, and structures rarely match exactly. Data transformation maps data from one system's format to another — a critical and often underestimated component of integration work.
Common Transformation Challenges
- Field mapping: "firstname" in system A is "first_name" in system B — straightforward renaming but multiplied across dozens of fields
- Value normalisation: System A stores phone numbers as "07700900000", system B requires "+447700900000" — format conversion
- Type conversion: Dates as Unix timestamps vs ISO 8601 strings vs "DD/MM/YYYY" formatted strings
- Enumeration mapping: System A status "ACTIVE" maps to system B status "1"
- Structural transformation: Flat records to nested objects, arrays to separate records, merging multiple source fields into one target field
- Default values: Required target fields that have no source equivalent — must be populated with appropriate defaults
Transformation Tools and Approaches
- Custom transformation code: Maximum control and flexibility — appropriate for complex logic
- JSONata: Declarative query and transformation language for JSON — powerful for structural transformations
- jq: Command-line JSON processor — good for pipeline transformations
- iPaaS mapping: Zapier, Make, and similar tools provide visual field mapping with some transformation logic
Data Quality in Transformations
Validate data quality at transformation boundaries. Missing required fields, out-of-range values, and invalid formats should be caught and handled — either rejected with a clear error or transformed to a safe default — before corrupt data propagates to the target system.