CSV to Parquet Converter

Convert CSV files to Parquet online for free. No upload required — runs entirely in your browser. Ideal for AWS S3, BigQuery, Spark, and other data platform workflows.

Drop a CSV file here, or click to select

Drop file here or click to browse

Choose File

Checks that make conversion safer

Before converting

Confirm the format required by the app, upload form, or person receiving the file.
Check details that may change during conversion, such as transparency, animation, or image quality.
Decide whether your main goal is compatibility, editing, or a smaller file size.

After converting

Open the converted image and check colors, text edges, and visible artifacts.
If the result is too large, continue with compression or resizing.
Use PNG for editing stages, and JPG or WebP when the final goal is sharing or publishing.

Need help choosing the right workflow?

The guide section covers format differences, compression tips, and common PDF workflows so you can choose the right tool with more context.

Browse guides

Common next steps

Image Compress

Useful when the converted file still needs to be lighter before upload or sharing.

Resize Image

A practical next step when the destination also has width or height requirements.

Image to PDF

Helpful when the converted images eventually need to be packaged into a PDF.

Why convert CSV to Parquet?

Parquet is a columnar storage format used widely across AWS S3, BigQuery, and Apache Spark. Compared to CSV, Parquet files are significantly smaller and faster to query, which reduces storage costs and query fees on pay-per-scan platforms like AWS Athena and BigQuery. Converting your CSVs to Parquet before uploading is a simple way to cut costs and improve pipeline performance.

Benefits of converting CSV to Parquet

Parquet uses columnar compression, which can reduce file size by 5–10x compared to CSV for typical datasets. On AWS Athena and BigQuery, you are billed based on the amount of data scanned, so smaller Parquet files translate directly into lower query costs.

Parquet also stores column data types, which means query engines can skip type inference. This speeds up queries and reduces the chance of type mismatch errors when loading data.

When to use this tool

Use it when uploading local CSVs to AWS S3 for Athena queries, when loading data into BigQuery or Redshift more efficiently, or when preparing input files for Spark or Flink jobs.

Since all processing runs in your browser, this tool is safe to use with sensitive or proprietary data.

Things to know before converting

All columns are written as STRING type. You can define a schema after loading into your data platform.
The first row is treated as the header row.
Snappy compression is used, which is broadly supported across AWS, GCP, and Spark.
Empty fields are treated as NULL.

CSV vs Parquet

Why convert

A conversion usually makes sense when the target format matches the next app, site, or workflow more naturally.

Tradeoff

Most format choices come down to tradeoffs between compatibility, compression, transparency, and editing behavior.

Best mindset

Start with the destination. Once you know where the file needs to end up, the right format is usually easier to pick.

Next step

After converting, people often continue with Parquet to CSV or PDF to JPG.

How to use

Upload your CSV file
Check the data preview and row/column counts
Click "Convert to Parquet"
Download the resulting Parquet file

FAQ

What tools can read the output Parquet file?

AWS Athena, BigQuery, Apache Spark, DuckDB, and pandas (with pyarrow) can all read the output. Snappy compression is used.

Is my data safe?

Yes. All processing runs locally in your browser. Your files are never uploaded to a server.

What happens to numeric or date columns?

All columns are written as STRING type. You can cast them when defining the table schema in Athena, BigQuery, or your data platform of choice.

What CSV encoding is supported?

UTF-8 encoded CSV files are supported. If your CSV uses a different encoding, convert it to UTF-8 first.

Is there a file size limit?

The limit depends on your browser's available memory. Most CSV files up to a few dozen megabytes should convert without issues.