Data Transformation
Converting data from one format, structure, or value system to another. Essential for integration when systems use different data models or formats.
In-Depth Explanation
Data transformation converts data from source format to target format. It's a core operation in data integration, ETL, and system connectivity.
Common transformations:
- Format conversion: JSON ↔ XML ↔ CSV
- Data type changes: String to date, number to currency
- Structural changes: Flatten nested data, pivot/unpivot
- Value mapping: Code translation, lookup replacement
- Aggregation: Sum, count, average
- Filtering: Include/exclude based on criteria
- Enrichment: Add data from other sources
- Cleansing: Standardise, deduplicate, validate
Transformation considerations:
- Data loss during conversion
- Handling null/missing values
- Maintaining data lineage
- Performance at scale
- Error handling
- Reversibility
Business Context
Data transformation bridges the gap between systems that speak different "languages". Good transformation ensures data is usable in the destination system.
How Clever Ops Uses This
We implement data transformations for Australian businesses as part of integration and migration projects, ensuring data quality throughout.
Example Use Case
"Transforming customer data from CRM format (with address as one field) to shipping system format (separate street, city, state, postcode fields)."