E-commerce Products (Parquet, 200 rows)
The e-commerce products table as Apache Parquet — the columnar twin, for testing analytics engines (pandas, DuckDB, Spark).
| 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 |
Specifications
- Rows
- 200
- Columns
- 7
- Format
- Apache Parquet
- 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 Novus Examples 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)Related files
- csvE-commerce Customers (CSV, 500 rows)A realistic e-commerce customer directory (500 rows) — part of a relational dataset (products, customers, orders) with CSV, JSON, SQL, and Parquet twins for testing joins, imports, and conversion.
- csvE-commerce Orders (CSV, 2000 rows)A realistic e-commerce order lines (customer_id → customers, product_id → products) (2000 rows) — part of a relational dataset (products, customers, orders) with CSV, JSON, SQL, and Parquet twins for testing joins, imports, and conversion.
- parquetE-commerce Orders (Parquet, 2000 rows)The e-commerce orders table as Apache Parquet — the columnar twin, for testing analytics engines (pandas, DuckDB, Spark).
- avroAvro — Row Binary + SchemaThe same records as Apache Avro — a compact row-based binary format that embeds its own schema, widely used in Kafka pipelines. For testing Avro decoders and schema evolution.
- orcConvert v2 ORC Employee Table SourceBinary orc source for the five-row P8 employee conversion table, preserving ids, names, departments, booleans, and scores. Stable P8 artifact p8-convert-orc-source.
- csvConvert v2 ORC Expected CSVCsv semantic reference for the five-row P8 employee conversion table, preserving ids, names, departments, booleans, and scores. Stable P8 artifact p8-convert-orc-csv.
Generated by generation/data_realworld.py. Free for any use, no attribution required — license.