Parquet vs CSV for Analytics — Which Format Should You Use?

Quick answer

Use Parquet for analytics and repeated querying. Use CSV when maximum portability and manual editing matter most.

Most teams keep Parquet as source format and export CSV only for sharing or one-off handoffs.

Performance and storage differences

Parquet stores data by column and compresses efficiently, so scans are faster and files are smaller for analytical queries.

CSV is row-based plain text, easy to inspect, but usually slower and larger at scale.

Decision framework

• Choose Parquet for BI models, warehouse ingestion, and large recurring datasets.
• Choose CSV for manual review, quick imports, and broad tool compatibility.
• Use both when you need a performant source plus an easy sharing format.

How to migrate safely

Convert a representative sample first, run SQL checks on row counts and key aggregates, then migrate full exports.

Example checks: SELECT COUNT(*) FROM dataset; and SELECT SUM(amount) FROM dataset;.

Related guides

CSV to Parquet migration workflow.

Parquet to CSV for downstream compatibility.