Sample Parquet File Download — Free Apache Parquet for Testing
Download free Apache Parquet example files from 100KB to 50MB — Snappy, GZIP, and uncompressed variants. These Parquet test files are built for data engineers and analysts working with Spark, Pandas, BigQuery, Athena, DuckDB, and Snowflake. Use them as parquet files for testing data lake ingestion, ETL pipelines, and columnar query performance.
sample-100kb.parquet
101 KB
1,100 rows · SNAPPY
File details and verification
- Filename
- sample-100kb.parquet
- Exact size
- 103,223 bytes
- Displayed size
- 101 KB
- Catalog status
- Valid catalog fixture
- SHA-256
- 233a2f1b5d5b59aef7b7082cca701bf85740d8f6bb0b9567f39cb60beff98dd9
- Header signature
- Matched (PAR1)
- Verified on
- 2026-08-10
- MIME type
- application/octet-stream
- Rows
- 1,100
- Columns
- 6
- Codec
- SNAPPY
- Note
- simple-flat
- License
- CC0 / Public Domain
- Download URL
- https://cdn.truefilesize.com/parquet/sample-100kb.parquet
See how TrueFileSize generates and measures sample files, or review the editorial policy.
sample-500kb.parquet
508 KB
3,300 rows · SNAPPY
File details and verification
- Filename
- sample-500kb.parquet
- Exact size
- 520,156 bytes
- Displayed size
- 508 KB
- Catalog status
- Valid catalog fixture
- SHA-256
- 9ba44fc83ee20f4b818eaa3a88a74a37616dbcb6ee614359c8a643ad35803024
- Header signature
- Matched (PAR1)
- Verified on
- 2026-08-10
- MIME type
- application/octet-stream
- Rows
- 3,300
- Columns
- 10
- Codec
- SNAPPY
- Note
- nested-schema
- License
- CC0 / Public Domain
- Download URL
- https://cdn.truefilesize.com/parquet/sample-500kb.parquet
See how TrueFileSize generates and measures sample files, or review the editorial policy.
sample-1mb.parquet
1.06 MB
5,000 rows · GZIP
File details and verification
- Filename
- sample-1mb.parquet
- Exact size
- 1,107,028 bytes
- Displayed size
- 1.06 MB
- Catalog status
- Valid catalog fixture
- SHA-256
- d688597090a38e8666256e4db34fb677b24616a7b44e19e9873b02b02629be4f
- Header signature
- Matched (PAR1)
- Verified on
- 2026-08-10
- MIME type
- application/octet-stream
- Rows
- 5,000
- Columns
- 12
- Codec
- GZIP
- Note
- with-nulls
- License
- CC0 / Public Domain
- Download URL
- https://cdn.truefilesize.com/parquet/sample-1mb.parquet
See how TrueFileSize generates and measures sample files, or review the editorial policy.
sample-5mb.parquet
5.15 MB
22,000 rows · SNAPPY
File details and verification
- Filename
- sample-5mb.parquet
- Exact size
- 5,396,990 bytes
- Displayed size
- 5.15 MB
- Catalog status
- Valid catalog fixture
- SHA-256
- e1bfb167a8381f610ba337902f098abceb29dfe0ae7de26eb42503b8035ddb7e
- Header signature
- Matched (PAR1)
- Verified on
- 2026-08-10
- MIME type
- application/octet-stream
- Rows
- 22,000
- Columns
- 15
- Codec
- SNAPPY
- Note
- large-columns
- License
- CC0 / Public Domain
- Download URL
- https://cdn.truefilesize.com/parquet/sample-5mb.parquet
See how TrueFileSize generates and measures sample files, or review the editorial policy.
sample-10mb.parquet
10.28 MB
44,000 rows · SNAPPY
File details and verification
- Filename
- sample-10mb.parquet
- Exact size
- 10,784,249 bytes
- Displayed size
- 10.28 MB
- Catalog status
- Valid catalog fixture
- SHA-256
- 44890c3142829fde6be7e6ffd4f7865202651248b83715e1364e7dea9c92c55e
- Header signature
- Matched (PAR1)
- Verified on
- 2026-08-10
- MIME type
- application/octet-stream
- Rows
- 44,000
- Columns
- 15
- Codec
- SNAPPY
- Note
- production-like
- License
- CC0 / Public Domain
- Download URL
- https://cdn.truefilesize.com/parquet/sample-10mb.parquet
See how TrueFileSize generates and measures sample files, or review the editorial policy.
sample-50mb.parquet
52.95 MB
150,000 rows · SNAPPY
File details and verification
- Filename
- sample-50mb.parquet
- Exact size
- 55,526,471 bytes
- Displayed size
- 52.95 MB
- Catalog status
- Valid catalog fixture
- SHA-256
- 9520de6bfef868cfff49cf086eb20706f9e3973ef85eb0b6a875da7375c25587
- Header signature
- Matched (PAR1)
- Verified on
- 2026-08-10
- MIME type
- application/octet-stream
- Rows
- 150,000
- Columns
- 20
- Codec
- SNAPPY
- Note
- stress-test
- License
- CC0 / Public Domain
- Download URL
- https://cdn.truefilesize.com/parquet/sample-50mb.parquet
See how TrueFileSize generates and measures sample files, or review the editorial policy.
sample-uncompressed.parquet
199 KB
2,000 rows · NONE
File details and verification
- Filename
- sample-uncompressed.parquet
- Exact size
- 203,702 bytes
- Displayed size
- 199 KB
- Catalog status
- Valid catalog fixture
- SHA-256
- c74a4a3778c4c7133cfa5410729796d6920a2dd2a3843b112ef70798d695691e
- Header signature
- Matched (PAR1)
- Verified on
- 2026-08-10
- MIME type
- application/octet-stream
- Rows
- 2,000
- Columns
- 8
- Codec
- NONE
- Note
- uncompressed
- License
- CC0 / Public Domain
- Download URL
- https://cdn.truefilesize.com/parquet/sample-uncompressed.parquet
See how TrueFileSize generates and measures sample files, or review the editorial policy.
sample-gzip.parquet
299 KB
3,000 rows · GZIP
File details and verification
- Filename
- sample-gzip.parquet
- Exact size
- 306,421 bytes
- Displayed size
- 299 KB
- Catalog status
- Valid catalog fixture
- SHA-256
- 33cc93c6a6e4a946429692ce4dd78ddf0b59516e8576c394ebc12367f65da727
- Header signature
- Matched (PAR1)
- Verified on
- 2026-08-10
- MIME type
- application/octet-stream
- Rows
- 3,000
- Columns
- 8
- Codec
- GZIP
- Note
- gzip-compressed
- License
- CC0 / Public Domain
- Download URL
- https://cdn.truefilesize.com/parquet/sample-gzip.parquet
See how TrueFileSize generates and measures sample files, or review the editorial policy.
Use cases for sample Parquet files
- Testing Parquet readers (pyarrow, DuckDB, Spark, pandas)
- Benchmarking Parquet vs CSV read performance
- Testing data lake ingestion pipelines (S3, GCS, ADLS)
- Verifying Parquet schema evolution and compatibility
- Testing BI tool Parquet import (Tableau, Power BI, Metabase)
- Validating Snappy vs GZIP compression handling
Parquet vs CSV vs JSON for analytics
| Feature | Parquet | CSV | JSON |
|---|---|---|---|
| Storage layout | Columnar | Row-based | Row-based |
| File size (1M rows) | ~50 MB | ~200 MB | ~400 MB |
| Column pruning | Yes (read only needed cols) | No (read all) | No (read all) |
| Schema enforcement | Yes (typed columns) | No (all strings) | Partial |
| Predicate pushdown | Yes (row group stats) | No | No |
| Human readable | No (binary) | Yes | Yes |
| Best for | Analytics, data lakes, ML | Data exchange, imports | APIs, configs |
How to read and write Parquet files
# Python (pandas + pyarrow — most common)
import pandas as pd
df = pd.read_parquet('data.parquet')
df.to_parquet('output.parquet', engine='pyarrow')
# Python (polars — faster alternative)
import polars as pl
df = pl.read_parquet('data.parquet')
# DuckDB (SQL on Parquet — zero copy)
duckdb.sql("SELECT * FROM 'data.parquet' WHERE age > 30")
duckdb.sql("COPY (SELECT * FROM my_table) TO 'out.parquet'")
# Apache Spark
df = spark.read.parquet("s3://bucket/data.parquet")
# CLI inspection (parquet-tools / pqrs)
parquet-tools schema data.parquet
parquet-tools head data.parquet
pqrs schema data.parquetParquet compression codecs
| Codec | Ratio | Speed | When to use |
|---|---|---|---|
| Snappy | Good | Very fast | Default — best balance (Spark, DuckDB) |
| GZIP | Best | Slow | Long-term storage, bandwidth-limited |
| ZSTD | Best | Fast | Modern alternative to GZIP (Spark 3+) |
| None | 1:1 | Fastest | Testing, already-compressed data |
Technical specifications
| Full name | Apache Parquet |
| Extension | .parquet |
| Type | Columnar binary storage format |
| Magic bytes | PAR1 (header and footer) |
| Compression | Snappy (default), GZIP, ZSTD, LZ4, Brotli, None |
| Encoding | Dictionary, RLE, Delta, Bit-packing |
| Nested types | Dremel-style repetition/definition levels |
| Developed by | Twitter + Cloudera (2013), Apache project |
Frequently Asked Questions
What is Apache Parquet?
Apache Parquet is an open-source columnar storage format designed for efficient analytics on large datasets. Unlike CSV (row-based), Parquet stores data column by column — enabling column pruning, better compression (4-10x smaller than CSV), and predicate pushdown. It's the default format for data lakes on S3/GCS/ADLS, used by Spark, BigQuery, Athena, Snowflake, and every major analytics platform.
Parquet vs CSV — Which is better for data?
Parquet is dramatically better for analytics: (1) 4-10x smaller files via columnar compression. (2) Column pruning — read only the columns you need. (3) Predicate pushdown — skip irrelevant row groups using min/max stats. (4) Schema enforcement — typed columns prevent data errors. CSV is better for human readability and simple data exchange. A 10GB CSV becomes ~1GB Parquet with 10-100x faster query performance.
Parquet vs Avro — What is the difference?
Parquet is columnar (optimized for read-heavy analytics — SELECT specific columns). Avro is row-based (optimized for write-heavy streaming and full-record access). Use Parquet for data warehouses, BI tools, and ad-hoc queries. Use Avro for Kafka event streams, data ingestion, and schema evolution. Many pipelines use Avro for ingestion → convert to Parquet for analytics.
How to read Parquet file?
Python: pd.read_parquet('data.parquet') or polars.read_parquet('data.parquet'). SQL: DuckDB — SELECT * FROM 'data.parquet'. Spark: spark.read.parquet('s3://bucket/data.parquet'). CLI: parquet-tools head data.parquet or pqrs schema data.parquet. Cloud: BigQuery, Athena, and Snowflake query Parquet directly from S3/GCS.
Parquet compression types — Snappy vs GZIP vs ZSTD?
Snappy (default): fastest decompression (5-10x faster than GZIP), ~20% larger files. Best for interactive queries in Spark/DuckDB/Presto. GZIP: best compression ratio but slowest. Best for cold storage and bandwidth-limited transfers. ZSTD: best of both worlds — near-GZIP compression with near-Snappy speed. Supported in Spark 3+, Arrow, DuckDB. Our sample files include Snappy, GZIP, and uncompressed variants.
How do I convert CSV to Parquet?
Python: pd.read_csv('data.csv').to_parquet('data.parquet', engine='pyarrow'). DuckDB: COPY (SELECT * FROM 'data.csv') TO 'data.parquet'. Spark: spark.read.csv('data.csv').write.parquet('output/'). Always specify column dtypes when converting to avoid all-string schemas in the output Parquet file.
Other data formats
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