Choose your next depth / OPTIONAL DEPTH
CSV, Parquet & schema evolution
Choose a storage format using types, access pattern and downstream compatibility.
Apply this to your Python data tools, PixelFed quality work, n8n integrations or BI delivery when the project needs it.
Understand the idea
CSV is convenient for inspection and interchange, but it does not carry a universal typed schema. Parquet stores typed columnar data and can support reading only the columns needed by an analysis. Performance depends on the actual file layout and workload.
Schema evolution is a compatibility decision. Adding an optional field differs from changing a business key or reducing decimal precision. Keep a written schema contract and test an older consumer against the changed dataset before promoting it.
What to learn
- Compare text CSV with typed columnar Parquet.
- Read selected columns and inspect schema metadata.
- Define compatible column changes, nullability and precision.
Practice on your own data
Export a copied synthetic fixture to Parquet, reload selected columns and compare values, nulls and timestamp interpretation.
Practice this lesson in the Python lab →
The round-trip report identifies every type conversion and the consumer can reproduce the expected row count and totals.
Prerequisites
Files, CSV, JSON & paths · DataFrames through a SQL lens · Joins, nulls & grain
Sources and further reading
- Apache Arrow · Parquet in Python ↗Official project documentation
- pandas · getting started tutorials ↗Official documentation
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