Guide

CSV vs Parquet: what changes at scale

CSV works well until data grows large. Parquet promises speed and efficiency, but switching formats introduces trade-offs. This guide explains what actually changes at scale and how to choose safely.

The problem

Format decisions are often made early and forgotten. At small sizes this rarely matters. At large sizes, format choice affects memory usage, processing speed, storage cost, and failure rates.

Why CSV breaks down

• Entire rows must be read even when few columns are needed
• Type inference happens repeatedly
• Compression is limited and inefficient
• Parallel processing is harder

What Parquet changes

• Column-based storage reduces unnecessary reads
• Data types are stored explicitly
• Compression works per column
• Partial reads become efficient

Side-by-side comparison

When CSV is still fine

• One-time transfers
• Small to medium files
• Manual inspection
• Early exploration

When Parquet is the safer choice

• Repeated analytics
• Partial column reads
• Memory-constrained systems
• Long-term storage

Failure modes

• Writing Parquet too early without stable schema
• Assuming Parquet fixes poor data modelling
• Mixing incompatible readers and writers

Decision checklist

• Is the data processed more than once?
• Are only some columns usually needed?
• Is memory usage a constraint?
• Is the schema stable?
• Is tooling available?