I wanted to peek at AWS Parquet files as CSV in the browser, so I built a tool

Kosuda / Filewisp — Thu, 16 Jul 2026 06:45:46 +0000

TL;DR

Working in AWS S3 / Athena, I constantly wanted to quickly inspect a Parquet file as CSV — and couldn't find a frictionless tool.
Installing a local toolchain or spinning up a query engine just to look at a file felt heavy.
So I built a browser-only Parquet ⇄ CSV converter and put it out for free (no upload, processed in the browser):
- Parquet Viewer: https://ai-image-tools.com/en/tools/parquet-viewer
- Parquet → CSV: https://ai-image-tools.com/en/tools/parquet-to-csv
- CSV → Parquet: https://ai-image-tools.com/en/tools/csv-to-parquet

Sharing in case you hit the same wall.

The problem

When you work with data on AWS, you keep running into moments where you just want to see what's inside a s3://.../part-0000.parquet:

Check the column names and a few rows before writing an Athena query
Debug a pipeline and confirm the emitted Parquet looks right
A non-engineer asks for it "in something I can open in Excel"

But Parquet is a binary, columnar format — you can't open it in a text editor or a normal spreadsheet. To peek inside, the options were:

pip install pandas pyarrow and write a script
Stand up Athena / Glue and run a query (and watch the scan cost)
Install a desktop app

…all heavy for "I just want a look." I wanted something closer to drop it in the browser and see a table.

Why go back and forth between Parquet and CSV

They play different roles:

	Parquet	CSV
Layout	Columnar	Row-based (text)
Size	Small (compresses well)	Large
Types	Typed per column	All text (guessed)
Human-readable	Hard to open	Opens in Excel etc.
Good for	Analytics & storage	Inspecting, sharing, handoff

Parquet wins for analytics and storage

In environments billed by data scanned — S3 + Athena, BigQuery — leaving data as CSV means scanning more. With Parquet:

Columns can be skipped → less scanned → lower query cost
Compression and column-oriented encoding → often materially smaller than CSV, depending on the dataset
Typed columns → fewer type-coercion and encoding headaches

Data that lives in a platform long-term is best stored as Parquet from the start.

CSV wins for a quick look

But when you "just want to see it once," want Excel, or need to hand it to a non-engineer, converting to CSV and opening locally is far faster than standing up a query engine.

So the rule of thumb becomes: Parquet for what the platform runs, CSV for checking and sharing — and you need a converter at that boundary.

What I built

To make "just let me see it" as short as possible, the converter runs entirely in the browser.

I published it as part of Filewisp, a collection of free browser-based tools for image, PDF, and data conversion.

Parquet → CSV: https://ai-image-tools.com/en/tools/parquet-to-csv
CSV → Parquet: https://ai-image-tools.com/en/tools/csv-to-parquet
Parquet Viewer: https://ai-image-tools.com/en/tools/parquet-viewer

Highlights:

No upload, in-browser processing — file contents stay in the browser instead of being sent to a conversion server.
Handles common codecs (Snappy / Gzip / Zstd).
Drop a file → see row/column counts and a preview → convert and download.
No install, free, works on mobile browsers.

The flow

Drop the .parquet you pulled from S3
Check the column and data preview
"Convert to CSV" → download

Going the other way, use CSV → Parquet to tidy a locally built CSV before loading it into a platform.

Honest caveats

Nested columns and Map types don't always flatten cleanly into CSV
NULLs become empty fields; dates/timestamps become strings
Size limit depends on browser memory and schema complexity. Split large files or use Athena/Spark for heavy workloads

It's built for inspecting and small-to-medium conversions, not heavy ETL.

A bit more depth

I wrote up the parts that didn't fit here:

What is Parquet? How it differs from CSV: https://ai-image-tools.com/en/guides/what-is-parquet
Parquet vs CSV workflows (AWS / BigQuery): https://ai-image-tools.com/en/guides/parquet-csv-workflows
Apache Parquet documentation: https://parquet.apache.org/docs/

Wrap-up

If "I want to turn an AWS Parquet into CSV right in the browser" has ever stopped you for a minute, I hope this helps. Feedback and bug reports welcome — I'll keep improving it.

DEV Community: Kosuda / Filewisp