Convert CSV to Parquet in your browser

Smaller files, faster analytics, typed columns. No upload, no row cap, no install. Powered by DuckDB-WASM.

When you'd convert CSV to Parquet

A few situations where Parquet is genuinely the right call:

Pipeline handoff to a warehouse. BigQuery, Athena, Snowflake, and Redshift all read Parquet directly and skip the cost of CSV parsing on every query. If your data ends up there, write Parquet.
Long-term storage of analytics exports. A 1 GB CSV that compresses to 150 MB as Parquet costs roughly an order of magnitude less to keep around, and the typed columns make re-reads predictable.
Sharing with notebooks (pandas, Polars, DuckDB). Parquet's per-column types mean the recipient doesn't have to redo type inference. Dates parse as dates, integers stay integers.
Selective column reads. If downstream queries only touch 3 of 50 columns, Parquet's columnar layout reads only those columns from disk. CSV reads every byte.
Reproducible exports from your warehouse. When you dump a query result, Parquet preserves the schema. CSV requires the recipient to guess.

Worked example

Say your input CSV is a small order log:

order_id,product,quantity,order_date
1001,Widget A,3,2026-01-15
1002,Widget B,1,2026-01-16
1003,Widget A,5,2026-01-16
1004,Widget C,2,2026-01-17

After conversion the Parquet file is binary, but logically it carries an explicit schema:

order_id   INT32
product    STRING (dictionary-encoded)
quantity   INT32
order_date DATE

On a real dataset the disk footprint typically drops to 20 to 35% of the CSV size. A query that asks for just SUM(quantity) reads the quantity column and skips the rest, which is what makes Parquet a good handoff format for analytics engines.

Format-specific gotchas

Type inference is best-effort. If a column mixes "42", "100", and "N/A", DuckDB will fall back to STRING. Either fix the bad rows, or override the type explicitly before exporting.
Empty string is not NULL. CSV doesn't distinguish between "" and a missing value. Parquet does. ExploreMyData preserves what's in the CSV; if you want NULLs, replace empty values in the column first.
Decimal precision. Numbers like "12.40" become FLOAT in Parquet by default. For currency or other exact-decimal cases, switch the column type to DECIMAL(precision, scale) before export.
Date parsing. "2026-01-15" parses as a DATE; "Jan 15, 2026" or "15/01/26" probably won't. Either standardise the CSV upstream, or coerce the column with a SQL expression in the explorer before exporting.
Compression choice. Snappy is the default (fast read, decent size). Zstd is smaller but slower to decode. For warehouse workloads stick with Snappy; for archive switch to Zstd.
Column names. Parquet itself accepts almost any UTF-8 string, but older Hive clients reject spaces and special characters. Rename to snake_case before export if you're targeting that ecosystem.

How this differs from the alternatives

Honest comparisons with the tools you'd otherwise reach for:

vs MyGeodata Cloud and DataConverter.io. Both are server-side: your CSV gets uploaded to their data centre, converted, and downloaded. DataConverter.io caps the free tier at 100 MB and requires sign-up. ExploreMyData runs in your browser, so the file never leaves your device and there's no cap.
vs ChatDB and Kanaries (browser-based). These also run client-side using DuckDB-WASM, so privacy is comparable. The difference is that they're single-purpose conversion pages with no preview or transform step. ExploreMyData wraps the same conversion in a real workspace, so you can filter, fix types, or run SQL before exporting.
vs pandas (pd.to_parquet). pandas is the most common Python idiom and works well, but you need Python plus pyarrow or fastparquet installed, and pandas loads the whole CSV into RAM. ExploreMyData uses the same Parquet writer (via DuckDB) and streams the conversion, so a multi-gigabyte CSV doesn't have to fit in memory.
vs DuckDB CLI. If you already have DuckDB installed, COPY (SELECT * FROM read_csv_auto('in.csv')) TO 'out.parquet' (FORMAT PARQUET); is hard to beat. ExploreMyData is the same engine without the install or the terminal.

Frequently Asked Questions

Why convert CSV to Parquet?

Parquet files are typically 3 to 10 times smaller than the equivalent CSV thanks to columnar storage and built-in compression. Analytics engines like BigQuery, Spark, Athena, Snowflake, DuckDB, and Polars also read Parquet much faster because they only fetch the columns they need.

Do I need Python or pandas to convert CSV to Parquet?

No. ExploreMyData uses DuckDB compiled to WebAssembly, so the conversion runs entirely in your browser. There's no install, no pip, and no command line. Drop the CSV onto the page, click Export, choose Parquet.

Is the resulting Parquet file compatible with BigQuery, Spark, and Athena?

Yes. ExploreMyData writes standard Apache Parquet using DuckDB's writer. The output is readable by BigQuery, Spark, Athena, Snowflake, DuckDB, Polars, pandas (with pyarrow or fastparquet), and any other Parquet-compatible tool.

How much smaller is Parquet than CSV in practice?

Real-world ratios are usually 3x to 10x. Columns with low cardinality (categories, status flags) compress especially well because Parquet uses dictionary encoding. A 1 GB CSV often becomes 100 to 200 MB as Parquet.

Can I control which compression Parquet uses?

Yes. The default is Snappy, which is fast to write and decode. For colder archive storage where you care about size more than read speed, switch to Zstd in the export dialog. Both are widely supported by downstream tools.

What about column types? Does it preserve them?

ExploreMyData infers column types from the CSV (integer, float, date, boolean, string) and writes them into the Parquet schema. You can review and override the inferred type before exporting if a column needs a specific representation, like DECIMAL for currency or TIMESTAMP for high-precision dates.

Convert your CSV to Parquet

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