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CSV → Parquet conversion plan

Converting to Parquet can reduce storage and speed up analytics, but the conversion itself can introduce silent errors. This plan focuses on safe conversion under large scale constraints.

Goal
Convert CSV to Parquet in a way that preserves meaning, keeps types stable, and produces outputs that can be trusted.
Tip: treat conversion as a pipeline step with validation, not as “just a format change”.

Step-by-step plan

  1. Step 1 — Inspect the CSV without full load Confirm file size, delimiter, header rules, and column count. Sample a small portion to detect obvious type and null patterns.
  2. Step 2 — Decide the schema explicitly Fix types before conversion. Mixed types across rows are common in CSV and lead to inconsistent Parquet columns.
  3. Step 3 — Choose a chunk strategy Read and convert in chunks. Keep chunk size conservative to avoid memory growth. Write each chunk immediately.
  4. Step 4 — Write Parquet with stable settings Use consistent compression and row group choices. Keep output partitioning simple at first (for example by date if time-based).
  5. Step 5 — Validate output correctness Validate row counts, null rates, key distributions, and type integrity. Confirm no silent truncation or parsing drift.
  6. Step 6 — Document the conversion Record schema, conversion settings, chunk size, errors handling, and validation results for reproducibility.

Validation checklist

  • Row counts are plausible Sample: full vs parquet totals. Accept small differences only if explained (dropped bad rows, etc).
  • Key columns have stable types No unexpected strings in numeric fields; no timestamp parsing drift.
  • Null rates did not change unexpectedly Null shifts often mean parsing errors or type coercion issues.
  • Basic distributions match Compare key metrics: min/max, mean/median, category proportions.

Common failure modes

  • Type inference differs between chunks, causing mixed Parquet types
  • CSV parsing changes because of quotes, commas, or broken rows
  • Partitioning creates too many small files
  • Validation is skipped, leading to silent errors

A safe default is: explicit schema + chunked conversion + minimal partitioning + basic validation.

Related reading
Guide 2: CSV vs Parquet Guide 1: clean large files Case: 100GB on a basic machine

Related

Links that help pick formats and validate outputs.

Guide: CSV vs Parquet Pipeline review checklist Send a problem statement

Also on this topic

Other views of format choice and scaling.

Guide: CSV vs Parquet: what changes at scale Case Note: 100GB processing on a basic machine
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