parquet7.6 KB

E-commerce Products (Parquet, 200 rows)

The e-commerce products table as Apache Parquet: the columnar twin, for testing analytics engines (pandas, DuckDB, Spark).

Data engineering Conversion testing

Preview: schema + first 8 rowsparquet

product_id	sku	name	category	price	stock	rating
1	SKU-00001	Wireless Coffee Beans	Home & Kitchen	222.23	216	4.4
2	SKU-00002	Deluxe Notebook	Books	487.92	47	4.5
3	SKU-00003	Classic Coffee Beans	Clothing	68.41	419	3.7
4	SKU-00004	Ergonomic Blender	Sports	323.7	463	4.6
5	SKU-00005	Stainless Yoga Mat	Toys	117.47	46	3.1
6	SKU-00006	Stainless Desk Lamp	Beauty	317.66	413	4.5
7	SKU-00007	Classic T-Shirt	Grocery	485.49	222	4.6
8	SKU-00008	Deluxe Coffee Beans	Electronics	236.02	97	3.1

Decoded Parquet: first 8 of 200 rows.

Specifications

Rows: 200
Columns: 7
Format: Apache Parquet
Domain: e-commerce

Testing contract

Expected to pass

Scenario: Exercise E-commerce Products (Parquet, 200 rows) in its ecommerce workflow. The e-commerce products table as Apache Parquet: the columnar twin, for testing analytics engines (pandas, DuckDB, Spark).
Expected result: 200 rows, 7 columns; fields: product_id: int64; sku: string; name: string; category: string; price: double; stock: int64; rating: double; column null counts=[0, 0, 0, 0, 0, 0, 0]. Declared feature checks: columns=7; domain=e-commerce.

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

“E-commerce Products (Parquet, 200 rows)” is a deterministic Testaroo fixture for Data engineering, Conversion testing. Columnar (Parquet/ORC/Feather), row (Avro), and messaging (MessagePack/CBOR/Protobuf) formats plus star-schema and log data, for testing ETL, data-lake ingestion, and warehouse loaders.

Documented properties for this file: 200 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("products.parquet")
print(df.head())
print(df.dtypes)

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