On this device
Local stays free
No account, no upload, and no change to the workflow you already use. DuckDB-WASM reads the file in this tab and IndexedDB can keep recent files on this browser.
Open local appSQL editor · S3 & URL · optional AI · private by default
Private online Parquet viewer & SQL workbench in your browser.
Explore Parquet at the speed of thought: open files from disk, S3, or a URL, run DuckDB SQL, and scan millions of rows without uploading anything. Free, no install, no account. Optional AI with your own key.
Parquet & GeoParquet first · CSV, JSON also supported
See what it doesviewparquet engineDuckDB · WebAssembly
trips_nyc_2024.parquet28,431,902 rows · 42 columns
ReadyYour dataset never passes through viewparquet servers.
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
- Local
- files stay here unless you choose cloud
- Stream
- Parquet from S3 & URLs
- WASM
- DuckDB running in your tab
- AI
- BYOK or managed cloud
Parquet guides
Start with the task you need to finish
Direct, practical guides for opening, inspecting, converting, and querying Parquet files.
Open & view
Open a Parquet file online
View rows, schema, metadata, and compression without Python or an install.
ConvertParquet to CSV
Filter the rows and columns you need, then export a spreadsheet-friendly CSV.
ConvertParquet to JSON
Preserve nested structs and lists in a local JSON Lines export.
ExcelOpen Parquet in Excel
Choose the quickest route through CSV export or Microsoft Power Query.
CloudQuery Parquet on S3
Read public, presigned, or private objects directly from your browser.
SpatialView GeoParquet
Inspect geometry columns, GeoParquet metadata, attributes, and SQL results.
Need a quick answer? Browse the Parquet FAQ.
S3 & URL
Query cloud Parquet without uploading it
Point viewparquet at a public HTTPS link, presigned URL, or s3:// object, or connect a private bucket with keys that stay in your browser. DuckDB streams only the byte ranges it needs, so large remote Parquet stays fast. Works with AWS S3, Cloudflare R2, Google Cloud Storage, and MinIO.
trips_nyc_2024.parquet
Open from S3 / URL
URLCredentials
Object URL
s3://analytics-prod/events/2024/trips.parquet
Amazon S3Cloudflare R2GCSMinIO
DuckDB streams byte ranges from the bucket — credentials sign requests in-browser and are never sent to viewparquet.
Local + cloud
One workbench, two clear places for your files
Keep the instant private local workflow, and add cloud only for the projects you want on every device.
ViewParquet Cloud
Sync selected projects
Explicitly save chosen files to private AWS S3, reopen them on another device, and use managed AI without an OpenRouter login or API key.
Private S3Managed AI
SQL editor
Real SQL, right in the browser
Write full DuckDB SQL with multi-tab queries, schema autocomplete, and one-click execution. Joins, CTEs, and 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
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 assistant
Ask in plain language, get SQL back
Describe what you need and the assistant drafts queries, explains your schema, and fixes errors, grounded in the file you have open. Bring your own key locally, use an existing browser agent, or choose managed AI with Cloud.
trips_nyc_2024.parquet
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
28,431,902 rows
Chat 1+
You
What is the average fare by payment type?
Assistant
Here is a grouped query on your open file:
SELECT payment_type, AVG(fare_amount) AS avg_fare FROM 'trips_nyc_2024.parquet' GROUP BY 1;
Ask anything about your data…
Private by default
Your data stays under your control
Local processing runs in your browser with DuckDB-WASM. Files upload only when you explicitly choose Save to cloud. Open your own S3 or URL directly, and control exactly what optional AI can see.
trips_nyc_2024.parquet
NetworkXHR/Fetch
NameSource
trips_nyc_2024.parquetdisk · local
s3://analytics/events.parquetstream · direct
duckdb-eh.wasmcache
openrouter.ai/api/v1/…optional · AI
No uploads to viewparquet — S3 & AI are direct from your browser
Open from S3 or a URL
LiveFormats
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?
Your datasets are processed locally with DuckDB WASM and never upload to viewparquet. Remote reads go directly from your browser to the bucket or URL. Optional AI sends only the context you allow, directly from your browser to your configured BYOK provider.
Can I run SQL on my data?
Yes. The SQL editor supports full DuckDB syntax with multi-tab queries, schema autocomplete, and exports. The AI assistant can draft queries, explain schema, and fix errors, grounded in the file you have open.
How does the AI assistant work?
Add your own OpenRouter/provider key for BYOK mode. Use data-sharing presets to control whether column names, types, metadata, or sample values are included.
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.
Can I open files from S3 or a URL?
Yes. Open data from a public HTTPS or presigned URL, a public s3:// object, or a private bucket using in-browser access keys (or temporary STS tokens), plus S3-compatible stores like Cloudflare R2, Google Cloud Storage, and MinIO. DuckDB streams only the bytes it needs; credentials stay in your browser and never pass through viewparquet servers.
Working with Parquet files? See the full Parquet FAQ : how to open, view, query, and convert Parquet.
Learn & Explore
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