Parquet for column-oriented analysis
Parquet is a column-oriented file format with a typed schema and compression. Analysis engines can read selected columns and, where supported by the file layout and query, skip irrelevant portions. That can suit repeated scans across many trading days.
File layout still matters. Partitioning, row groups, sort order and query shape affect the work an engine performs. A format alone does not establish data quality or a universal speed advantage.
CSV for broad text compatibility
CSV is widely supported and easy to inspect with ordinary tools. Its text representation requires a reader to interpret timestamps, numbers and missing values. When you convert a CSV archive, define the schema explicitly and preserve the source convention.
FirstRate supplies zipped CSV archives; Kibot documents comma-delimited text products. They are worth comparing when an existing analysis platform expects those formats.
Audit semantics before benchmarking speed
Compare the same symbol, dates, session and adjustment variant before measuring loading time. Faster queries over a different universe do not make a fair benchmark. Save a small fixture with known rows and compare it after every conversion.
Make delivery part of the buying decision
MarketParquet supplies data for pandas, Polars and DuckDB workflows without a CSV-to-Parquet conversion step. If your engine already expects Parquet, that is one reason it is our top pick. Confirm the actual schema and file organization with a sample.
Sources & freshness
Provider information checked November 7, 2026. Confirm current scope and terms before purchase.