parquet31.5 KB

E-commerce Orders (Parquet, 2000 rows)

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

Data engineering Conversion testing

Preview: schema + first 8 rowsparquet

order_id	customer_id	product_id	quantity	total	status	order_date
1	150	171	2	705.98	paid	2025-10-19
2	174	36	1	25.59	refunded	2025-06-09
3	31	1	2	444.46	shipped	2025-04-27
4	293	51	4	881.44	delivered	2025-08-22
5	431	127	3	1105.89	shipped	2025-03-12
6	295	190	5	234.85	delivered	2025-05-03
7	336	52	5	2229.65	refunded	2025-05-26
8	236	54	4	207	pending	2025-04-29

Decoded Parquet: first 8 of 2000 rows.

Specifications

Rows: 2000
Columns: 7
Format: Apache Parquet
Domain: e-commerce

Testing contract

Expected to pass

Scenario: Exercise E-commerce Orders (Parquet, 2000 rows) in its ecommerce workflow. The e-commerce orders table as Apache Parquet: the columnar twin, for testing analytics engines (pandas, DuckDB, Spark).
Expected result: 2000 rows, 7 columns; fields: order_id: int64; customer_id: int64; product_id: int64; quantity: int64; total: double; status: string; order_date: string; 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 Orders (Parquet, 2000 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: 2,000 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("orders.parquet")
print(df.head())
print(df.dtypes)

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