Tick Data CSV To Parquet

Our CSV → Parquet Tick Data Converter takes raw trade/quote files and writes analytics-ready Parquet you can query instantly (Python/pandas, Polars, DuckDB, Spark) without waiting on slow CSV parsing.

Why Parquet is better than CSV (especially for tick data)

Smaller files (often 3–10× less storage) Parquet is columnar + compressed, so repetitive fields like symbols, exchanges, flags, and timestamps compress extremely well.

Much faster reads for research With columnar storage you read only what you need (e.g., ts, price, size) instead of scanning every column like CSV.

Predicate pushdown + row-group skipping Filters like “only NQ”, “only 09:30–10:00”, “only trades” can skip large chunks of data on disk—huge speedup for backtests and feature jobs.

Real types, fewer parsing headaches Parquet stores schema (int64, float32, timestamp, categorical), so you stop re-parsing strings and fighting dtype drift across files.

Plays nicely with modern tooling Built for Arrow/DuckDB/Polars/Spark—great for joining, aggregating, and streaming through massive datasets.

More reproducible pipelines Schema + metadata means fewer “works on my machine” moments and more consistent downstream stats.

Bottom line: CSV is great for interchange; Parquet is built for speed, scale, and analytics. Converting your tick archive to Parquet usually pays for itself the first time you run a multi-day query or a full backtest.