Free Parquet File Viewer
Inspect Apache Parquet file metadata in your browser. View schema, row counts, column statistics, and file structure. Free, fast, and works entirely in your browser with no sign-up required.
Updated
Parquet File Viewer
Inspect Apache Parquet file metadata, schema, and structure in your browser.
Parquet File Viewer
Read Row Data with Code
Full row data reading requires a Parquet library. Here are code snippets for common environments:
import pandas as pd
# Read a Parquet file
df = pd.read_parquet('your_file.parquet')
print(df.head())
print(df.dtypes)About Apache Parquet
Apache Parquet is a columnar storage format designed for big data workloads. It stores data column-by-column rather than row-by-row, enabling efficient compression and predicate pushdown.
Files begin and end with the magic bytes PAR1. File metadata is Thrift-encoded and stored in the footer before the trailing magic.
Frequently Asked Questions
What is the Parquet File Viewer?
The Parquet File Viewer is a free online tool that inspect apache parquet file metadata in your browser. view schema, row counts, column statistics, and file structure. It runs entirely in your browser with no installation or sign-up needed.
What information can I see?
File schema, column types, row group count, total rows, compression codec, and column statistics (min/max).
Can I view row data?
Metadata is shown directly. For full row data, the tool provides code snippets for Python, Node.js, and DuckDB-WASM.
Is it free?
Yes, completely free.
Does the Parquet File Viewer work on mobile devices?
Yes, the Parquet File Viewer is fully responsive and works on smartphones and tablets. You can use it on any device with a modern web browser -- no app download required.
Do I need to create an account to use this tool?
No account or registration is needed. Simply open the Parquet File Viewer in your browser and start using it immediately. There are no sign-up walls or usage restrictions.
How do I use the Parquet File Viewer?
Simply enter your input in the provided field, adjust any settings to your preference, and the tool will process it instantly. You can then copy the result to your clipboard or download it.
Which browsers are supported?
The Parquet File Viewer works in all modern browsers including Chrome, Firefox, Safari, Edge, and Opera. For the best experience, use the latest version of your preferred browser.
Why can a Parquet viewer show the schema and row count of a huge file instantly without reading it all?
Parquet stores its structural information in a Thrift-encoded footer at the very end of the file, framed by the magic bytes PAR1 at both the start and end. That footer holds the schema, total row count, row-group boundaries, compression codec, and any embedded key-value metadata. Because all of this lives in one compact block, a viewer only needs to read the footer length, jump to the footer, and decode it, rather than scanning gigabytes of column data. That is why a multi-gigabyte file describes itself almost as fast as a tiny one, and why you can sanity-check structure before any heavy processing. This tool reads that footer locally in your browser, so even large files report their schema and structure in seconds. Drop a .parquet file above to see the overview without opening a single row of data.
What is the difference between a Parquet row group and a column?
A column is a single field in your schema, such as user_id or created_at, stored together with all of its values for fast compression and column pruning. A row group is a horizontal slice of the whole table: it bundles a chunk of rows across every column into one self-contained unit with its own statistics and on-disk size. A file with one million rows might split them into several row groups, and each group still contains all the columns. Row groups let query engines skip entire blocks they do not need using min and max statistics, which is what makes predicate pushdown effective. This viewer reports both views: a Schema tab listing every leaf column with its type, and a Row Groups tab showing per-group row counts and sizes. Upload a file above to compare its column layout against its row-group partitioning.
What does the created_by field in a Parquet file tell me?
The created_by string records which library and version wrote the file, for example a particular parquet-mr, Arrow, or pandas release. It is stored in the footer metadata alongside the format version. This is genuinely useful when debugging: certain writers have known quirks with statistics, timestamp encoding, or INT96 handling, so knowing the producer helps you explain odd behavior or decide whether to re-export with a newer toolchain. It also confirms provenance when a file is handed to you with no context, telling you whether it came from Spark, DuckDB, Polars, or a pandas script. This viewer surfaces created_by directly in the File Overview, next to the format version and compression codec, so you can check the writer before pulling the file into a pipeline. Upload a .parquet file above to read its created_by string.
Which compression codecs does Parquet support, and how do I check which one a file uses?
Parquet compresses each column chunk independently, and the format defines several codecs: UNCOMPRESSED, SNAPPY, GZIP, LZO, BROTLI, LZ4, ZSTD, and LZ4_RAW. SNAPPY is a common default because it balances speed and ratio, while ZSTD often yields smaller files at a modest CPU cost. The codec is recorded in each column's metadata inside the footer, so you do not need to decompress anything to find out which one was used. Knowing the codec matters because a reader that lacks support for, say, BROTLI or LZ4_RAW will fail to open the file, and an unexpected codec can quietly bloat or shrink storage. This viewer reads the codec from the footer and displays it in the File Overview as a badge. Upload your file above to confirm its compression before a job depends on it.
How do I actually read the row values from a Parquet file after checking its schema here?
This viewer inspects metadata rather than dumping cell values, which keeps footer parsing fast and lightweight. To read the rows themselves you need a full Parquet decoder, so the tool provides ready-to-copy code snippets for three common environments. In Python, pandas reads a file in one line with pd.read_parquet, the usual path for analysts. In Node.js, the parquetjs-lite reader streams records through a cursor. With DuckDB-WASM you can run SQL such as read_parquet('your_file.parquet') directly in the browser, no server required. Each snippet is a working starting point: copy it, point it at your file, and adjust from there. The recommended flow is to check the schema, types, and row count here first, then grab the snippet that matches your stack. Upload a file above, review the structure, then copy the code that fits your workflow.
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About the Parquet File Viewer
The Parquet File Viewer is a free tool for inspecting the structure of an Apache Parquet file without writing a line of code. Drop in a .parquet file and it reads the file's footer metadata to show you the schema, row count, row groups, compression codec, and any embedded key-value metadata. It is built for data engineers, analysts, and developers who receive a Parquet file and need to answer a quick question — what columns are in here, how many rows, and how was it written — before pulling it into a pipeline or notebook.
Everything happens in your browser. The file is read with the browser's FileReader and parsed locally, so your data never leaves your device and there is no upload, no account, and no size limit imposed by a server. That matters when the file holds customer records, financial data, or anything you would not paste into a random website.
What the viewer reads from a Parquet file
Parquet stores its metadata in a Thrift-encoded footer at the end of the file, framed by the magic bytes PAR1 at both the start and the end. The viewer locates that footer and decodes it into a readable overview:
- File overview — total file size, total row count, the number of row groups, and the number of leaf columns, shown as a quick summary.
- Format details — the Parquet format version, the
created_bystring (the writer that produced the file, such as a specific Arrow or parquet-mr version), and the compression codec. - Schema — every leaf column with its name, physical type, and repetition. Physical types include
BOOLEAN,INT32,INT64,INT96,FLOAT,DOUBLE,BYTE_ARRAY, andFIXED_LEN_BYTE_ARRAY. Repetition is one ofREQUIRED,OPTIONAL, orREPEATED, which tells you whether a column allows nulls or holds repeated values. - Row groups — a per-group breakdown of row count, on-disk size, and column count, so you can see how the file was partitioned internally.
- Key-value metadata — any custom metadata the writer embedded, which often includes the Arrow schema or framework-specific notes.
Recognized compression codecs include UNCOMPRESSED, SNAPPY, GZIP, LZO, BROTLI, LZ4, ZSTD, and LZ4_RAW.
Reading the actual row data
This viewer inspects metadata, not cell values — it does not dump the rows. That is a deliberate trade-off: parsing the footer is fast and lightweight, while reading full row data requires a complete Parquet decoder. To get the values themselves, the tool gives you ready-to-copy code snippets for three common environments:
- Python with pandas (
pd.read_parquet), the most common path for analysts. - Node.js using the
parquetjs-litereader. - DuckDB-WASM, which can query a Parquet file with SQL directly in the browser via
read_parquet('file.parquet').
Copy the snippet, point it at your file, and you have a working starting point in seconds.
Why Parquet metadata is worth checking first
Apache Parquet is a columnar storage format built for analytics and big-data workloads. Instead of storing data row by row, it stores each column together, which makes compression far more effective and lets query engines skip columns and row groups they do not need — a technique called predicate pushdown. Because the schema and statistics live in the footer, a tool can describe a multi-gigabyte file almost instantly without scanning it.
Checking the metadata first saves real time. You can confirm a column was written with the type you expected, spot an unexpected codec before a job fails, sanity-check the row count against what an upstream export should have produced, or read the created_by field to figure out which tool generated a file you have been handed. For anyone debugging a data pipeline, those answers usually come before opening the rows at all.
Upload a .parquet file above to see its schema and structure, then grab a code snippet if you need to read the rows themselves.