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Converts to Apache Parquet on secure servers · first daily conversion free · no signup

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Pack tabular CSV into a compact, columnar Apache Parquet file with type inference and Snappy compression. Drop-in ready for Spark, DuckDB, Polars, and pandas pipelines.

By ChangeThisFile Team · Last updated: March 2026

Quick Answer

ChangeThisFile converts CSV to Apache Parquet by parsing the header row, type-inferring each column (int, float, boolean, string, ISO timestamp), and writing a single-row-group Parquet file with Snappy compression. The result is typically 5-10× smaller than the source CSV and natively readable by Spark, DuckDB, Polars, and PyArrow. Free, encrypted upload, files auto-deleted after conversion.

First daily conversion free No signup required Encrypted transfer · Async jobs kept up to 24h Queued when needed Updated May 2026

CSV vs Apache Parquet: Format Comparison

Key differences between the two formats

Feature	CSV	Parquet
Storage layout	Row-oriented text	Column-oriented binary
Schema	Implicit (string values)	Embedded, strongly typed
Compression	None (compress externally)	Snappy by default, also GZIP/ZSTD/LZ4
Typical file size	Baseline	5-10× smaller (column compression)
Query speed	Full scan	Column projection + predicate pushdown
Type fidelity	All strings	INT, DOUBLE, BOOLEAN, TIMESTAMP, etc.
Best for	Sharing, debugging, Excel	Analytics, data lakes, repeated queries

When to Convert

Common scenarios where this conversion is useful

Compressing large CSV exports for cold storage

A 1GB CSV often shrinks to 100-200MB as Parquet, with the bonus that queries against it skip irrelevant columns entirely.

Loading data into a Spark or DuckDB pipeline

Spark, DuckDB, and Polars all read Parquet faster than CSV. Convert once, query many times — cheaper than re-parsing CSV on every job.

Building a partitioned dataset

Convert each CSV partition to Parquet before uploading to S3. Combined with a Hive-style folder layout, downstream tools can predicate-push date filters.

Faster pandas loads

`pd.read_parquet` is dramatically faster than `pd.read_csv` and preserves dtypes. Convert once, then reload in seconds during exploratory analysis.

Who Uses This Conversion

Tailored guidance for different workflows

Developers

Convert CSV config files to Apache Parquet for compatibility with different tools or frameworks
Transform CSV API responses to Apache Parquet for debugging, logging, or documentation

Validate the converted Apache Parquet output with a linter to catch any structural issues from the conversion

Watch for data type coercion (e.g., numbers becoming strings) when converting between CSV and Apache Parquet

Data Analysts

Convert CSV exports to Apache Parquet for importing into spreadsheet software, databases, or BI tools
Transform CSV datasets to Apache Parquet for sharing with teammates who use different analysis tools

Check that column delimiters and quote escaping are handled correctly in the converted Apache Parquet file

Preview the first few rows of the Apache Parquet output to verify headers and data alignment

System Administrators

Convert CSV configuration to Apache Parquet when migrating between infrastructure tools or platforms
Transform CSV log exports to Apache Parquet for ingesting into monitoring or analysis systems

Back up the original CSV config before converting, especially for production systems

Test the converted Apache Parquet in a staging environment before deploying to production

How to Convert CSV to Apache Parquet

1

Upload your CSV file

Drop your .csv into the converter. Auto-detected delimiters (comma, semicolon, tab, pipe), up to 50MB per upload.
2

Server-side type inference and encoding

The first row is treated as the column header. Each column is sampled to infer a type (int, double, boolean, ISO timestamp, or string), then encoded column-wise with Snappy compression.
3

Download the Parquet file

Your .parquet file is delivered as a download. The uploaded CSV is deleted from disk immediately after conversion.

Automate this conversion

Convert CSV to Apache Parquet via API

Integrate this conversion into your pipeline with 3 lines of code. Free tier: 25 conversions/month.

curl -X POST https://changethisfile.com/v1/convert \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -F "file=@input.csv" \
  -F "target=parquet" \
  -o output.parquet --fail

const form = new FormData();
form.append('file', fileInput.files[0]);
form.append('target', 'parquet');

const res = await fetch('https://changethisfile.com/v1/convert', {
  method: 'POST',
  headers: { 'Authorization': 'Bearer YOUR_API_KEY' },
  body: form
});
const blob = await res.blob(); // your converted Apache Parquet file

Replace YOUR_API_KEY with your free key — no credit card needed.

Get a free API key

Frequently Asked Questions

How is the Parquet schema inferred?

Column names come from the first row. Types are inferred by sampling the column: all values parse as integers → INT64; all parse as numbers → DOUBLE; all are true/false → BOOLEAN; all are ISO 8601 timestamps → TIMESTAMP; otherwise STRING. Mixed or ambiguous columns fall back to STRING.

Can I control column types explicitly?

Not via the anonymous web converter. For an explicit schema, cast columns in your CSV (e.g., quote numeric IDs to force STRING) or use a tool like `duckdb` or `pyarrow` post-conversion to recast.

What compression is used?

Snappy by default — a good balance of ratio and decode speed, and the de facto default in the Spark/Arrow ecosystem. The output file is a single row group; for very large datasets you may want to repartition with PyArrow afterward.

Will the output work with Spark / DuckDB / pandas / Polars?

Yes. The output is standard Apache Parquet with a Thrift footer, compatible with PyArrow 8+, DuckDB 0.7+, Spark 3.x, and Polars. If a downstream tool can read Parquet, it can read this.

How are nulls represented?

Empty CSV cells (between two consecutive delimiters) become NULL in the Parquet output. Cells with the literal string "null" stay as the string "null" in a STRING column. If you need a different sentinel, normalize the CSV first.

What about quoted fields and embedded commas?

RFC 4180 quoting is honored. Quoted fields can contain commas, newlines, and escaped quotes (""). Auto-detection covers comma, semicolon, tab, and pipe delimiters.

Is there a row or file size limit?

50MB per upload on the anonymous endpoint, 5 requests per minute per IP. For larger files, use the authenticated /v1/convert API or pre-split with `split` or `csvkit`.

Are my files private?

Yes. HTTPS upload, processed in an ephemeral temp directory, deleted immediately after the response. Contents are not logged.

Why is this server-side?

Writing Parquet requires Snappy compression, Thrift encoding, and column statistics — heavy work that would mean a multi-megabyte WASM bundle in the browser. Server-side keeps the page fast.

Related Conversions

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