Data format conversion
Getting data into the format your systems expect, without losing anything, is a routine but error-prone task. This guide covers data format conversion for delivery.
Why conversion matters
Source and target rarely share a format, so conversion is part of almost every delivery. Done carelessly, it loses precision, structure or meaning; done well, it is invisible.
Common conversions
- To Parquet: for analytical scale.
- To CSV/Excel: for interchange and business users.
- To JSON: for application integration.
- Nested vs tabular: structure changes.
Fidelity traps
Watch for precision loss (numbers, dates), encoding issues (text), structure flattening (nested to tabular), and type coercion. Validation after conversion catches these.
Match format to consumer
The right format follows the consumer and volume: Parquet for warehouses, CSV/Excel for people, JSON for apps. Converting to the wrong format creates downstream friction.
Sourcing considerations
Conversion should preserve schema and semantics, with validation and documentation. Large-scale conversion benefits from efficient columnar formats.
In a managed model
A managed partner can convert data to your required formats with fidelity checks and documentation.
Preserving fidelity
Format conversion is part of almost every delivery, and done carelessly it loses precision (numbers, dates), corrupts encoding (text), flattens structure (nested to tabular) or coerces types. Validation after conversion catches these, and preserving schema and semantics, not just moving bytes, is what makes a conversion invisible rather than damaging.
Match format to consumer
The right target follows the consumer and volume: Parquet for warehouses, CSV or Excel for people, JSON for applications. Converting to the wrong format creates downstream friction, so the conversion decision is really a delivery-design decision.
- Conversion is part of almost every delivery.
- Watch precision, encoding, structure and type traps.
- Match format to consumer and volume.
- Validate and document after conversion.
Sources & further reading
- Apache Parquet and Arrow documentation.
- RFC and standards for CSV and JSON.
- DAMA-DMBOK: data delivery.
- Internal practice: DataSupplier delivery.
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