viewparquet
    BlogOpen app
    SQL editor · live now · AI assistant · coming soon

    Explore Parquet at the speed of thought.

    Free online Parquet viewer — private, browser-based, no uploads.

    Open a file, run SQL, and scan millions of rows — entirely in your browser. Powered by DuckDB-WASM, with zero server uploads.

    See what it does

    Parquet & GeoParquet first · CSV, JSON also supported

    trips_nyc_2024.parquet
    Query 1fares_by_type
    SELECT payment_type,
    AVG(fare_amount) AS avg_fare
    FROM 'trips_nyc_2024.parquet'
    GROUP BY 1 ORDER BY 2 DESC;
    trip_id
    pickup_time
    fare_amount ▲
    payment_type
    1
    t_8f3a21
    2024-03-14 08:12:04
    24.50
    card
    2
    t_8f3a22
    2024-03-14 08:12:11
    18.75
    card
    3
    t_8f3a23
    2024-03-14 08:12:18
    42.20
    cash
    4
    t_8f3a24
    2024-03-14 08:12:25
    9.80
    card
    5
    t_8f3a25
    2024-03-14 08:12:31
    56.30
    card
    6
    t_8f3a26
    2024-03-14 08:12:38
    31.00
    card
    28,431,902 rows

    Built for data teams that live in columnar files

    0
    bytes uploaded to a server
    <100ms
    typical query on large files
    WASM
    DuckDB running in your tab
    100%
    client-side processing
    SQL editor

    Real SQL, right in the browser

    Write full DuckDB SQL with multi-tab queries, schema autocomplete, and one-click execution. Joins, CTEs, window functions — then export results to Parquet, CSV, or JSON.

    trips_nyc_2024.parquet
    Query 1fares_by_type
    WITH by_type AS (
    SELECT payment_type, AVG(fare_amount) AS avg_fare,
    COUNT(*) AS trips
    FROM 'trips_nyc_2024.parquet'
    GROUP BY 1
    )
    SELECT * FROM by_type ORDER BY avg_fare DESC;
    payment_type
    avg_fare
    trips
    card
    34.18
    21,902,114
    cash
    27.55
    6,201,338
    no charge
    0.00
    328,450
    3 rows
    Results grid

    Scan millions of rows without the wait

    A virtualized grid scrolls huge result sets smoothly, with type-aware coloring and per-column stats — cardinality, ranges, and null counts at a glance.

    trips_nyc_2024.parquet
    Column stats
    fare_amount
    DECIMAL
    min 0.00 · max 412.50
    null 0.02%
    payment_type
    VARCHAR
    4 distinct
    trip_id
    pickup_time
    fare_amount ▲
    payment_type
    1
    t_8f3a21
    2024-03-14 08:12:04
    24.50
    card
    2
    t_8f3a22
    2024-03-14 08:12:11
    18.75
    card
    3
    t_8f3a23
    2024-03-14 08:12:18
    42.20
    cash
    4
    t_8f3a24
    2024-03-14 08:12:25
    9.80
    card
    5
    t_8f3a25
    2024-03-14 08:12:31
    56.30
    card
    6
    t_8f3a26
    2024-03-14 08:12:38
    31.00
    card
    7
    t_8f3a27
    2024-03-14 08:12:44
    12.40
    cash
    8
    t_8f3a28
    2024-03-14 08:12:50
    88.10
    card
    28,431,902 rows
    AI assistantComing soon

    Ask in plain language, get SQL back

    Describe what you need and the assistant will draft queries, explain your schema, and fix errors — grounded in the file you have open. Run generated SQL or insert it into the editor.

    Coming soon
    trips_nyc_2024.parquet
    Coming soon

    Ask in plain language, get SQL back — grounded in the file you have open.

    Private by default

    Your file never leaves your machine

    Every byte stays in your browser with DuckDB-WASM — zero uploads, zero accounts. Open the Network panel and watch nothing leave. Optional cloud storage sync is on the roadmap.

    trips_nyc_2024.parquet
    NetworkXHR/Fetch
    NameSource
    trips_nyc_2024.parquetdisk · local
    duckdb-eh.wasmcache
    Files load from disk — no uploads to a server
    Optional cloud storage sync
    Coming soon
    Formats

    Parquet-first, GeoParquet-ready.

    Purpose-built for columnar files, with CSV and JSON supported when you need them — all processed locally in DuckDB-WASM.

    Parquet.parquet
    Native columnar
    First-class
    GeoParquet.geoparquet
    With geometry
    First-class
    CSV.csv
    Auto-detected schema
    Supported
    TSV.tsv
    Tab-delimited
    Supported
    JSON.json
    Objects or arrays
    Supported
    JSON Lines.jsonl
    One record per line
    Supported
    FAQ

    Questions, anticipated.

    Got questions? Here are the most common ones about viewparquet.

    Do my files leave the browser?

    No. All processing happens locally using DuckDB WASM. Check the Network panel — zero uploads.

    Can I run SQL on my data?

    Yes. The SQL editor supports full DuckDB syntax with multi-tab queries, schema autocomplete, and exports. An AI assistant to generate SQL, explain schema, and fix errors is coming soon.

    What file types are supported?

    Parquet and GeoParquet are first-class formats for the fastest path. CSV, TSV, JSON, and JSON Lines are also supported as secondary formats.

    Is there a file size limit?

    File size is only limited by your browser's available memory. The viewer streams and paginates for efficiency.

    When is cloud storage coming?

    Cloud storage (S3, GCS, and more) is on the roadmap. Today viewparquet is private-first — all processing runs client-side in your browser.

    Working with Parquet files? See the full Parquet FAQ — how to open, view, query, and convert Parquet.

    Changelog

    2.0

    SQL editor & results grid

    1.2

    Saved files & faster Parquet loads

    1.1

    GeoParquet & export to CSV / JSON

    1.0

    Browser-native Parquet viewer

    See what's new in viewparquet
    Learn & Explore

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