parquet2.4 KB

Parquet: Columnar Table

A small employee table as Apache Parquet: the columnar format at the heart of modern data lakes and analytics. For testing Parquet readers (pandas, Spark, DuckDB) and conversion.

Conversion testing Data engineering

Preview: schema + first 5 rowsparquet

idint64	namestring	emailstring	departmentstring	activebool	scoredouble	joineddate
1001	Ada Lovelace	ada.lovelace@example.com	Engineering	true	98.5	2021-03-01
1002	Alan Turing	alan.turing@example.com	Research	true	95	2020-06-15
1003	Grace Hopper	grace.hopper@example.com	Engineering	false	91.2	2019-11-20
1004	Katherine Johnson	katherine.johnson@example.com	Operations	true	96.8	2022-01-10
1005	Edsger Dijkstra	edsger.dijkstra@example.com	Research	false	89.4	2018-09-05

Decoded table: all 5 rows shown.

Specifications

Rows: 5
Columns: 7
Format: Apache Parquet
Layout: columnar
Compression: snappy

Testing contract

Expected to pass

Scenario: Exercise Parquet: Columnar Table in its binary workflow. A small employee table as Apache Parquet: the columnar format at the heart of modern data lakes and analytics.
Expected result: 5 rows, 7 columns; fields: id: int64; name: string; email: string; department: string; active: bool; score: double; joined: string; column null counts=[0, 0, 0, 0, 0, 0, 0]. Declared feature checks: columns=7; layout=columnar; compression=snappy.

What is a .parquet file?

Apache Parquet (.parquet) is a binary, columnar storage format for analytical data. It stores each column separately with per-column compression and encoding, embeds a schema and statistics, and is the de-facto standard for data lakes and engines like Spark, DuckDB, and pandas/pyarrow.

How to use this file

Use an example .parquet file to test columnar readers (pyarrow, DuckDB, Spark), schema and predicate-pushdown handling, and Parquet-to-CSV/JSON converters.

How to use this file for testing

“Parquet: Columnar Table” is a deterministic Testaroo fixture for Conversion testing, Data engineering. The same content exported across many formats and linked as a group, so you can convert one and diff against the expected twin.

Documented properties for this file: 5 rows · 7 columns · Apache Parquet. Compare results against paired or grouped companions on this page when present (clean↔damaged, searchable↔scanned, or format twins) so scores stay reproducible across runs.

Download the file once, keep the path stable in CI or local scripts, and treat the spec table as the contract: dimensions, seeds, field lists, and roles are intentional. Corrupt or invalid samples are labelled as such, expect parsers to fail loudly rather than silently accept them.

Data fixtures document their exact quirks (delimiters, encodings, null handling, schema, and row counts) in the spec table. Point your parser or importer at the file and assert it handles the documented edge cases; clean and deliberately-messy siblings make before/after diffs straightforward.

Code examples

import pandas as pd  # pip install pyarrow

df = pd.read_parquet("employees.parquet")
print(df.head())
print(df.dtypes)

Generated by generation/data_binary.py. Free for any use, no attribution required, license.