csv918 B
Star Schema — Product Dimension (CSV)
The product dimension of a star schema — surrogate key, SKU, name, category, and unit price. Joins to the sales fact table on product_key.
Preview — first 22 linescsv
product_key,sku,name,category,unit_price
1,SKU-0001,Wireless Mouse,Peripherals,177.31
2,SKU-0002,Mechanical Keyboard,Peripherals,148.53
3,SKU-0003,USB-C Hub,Accessories,271.85
4,SKU-0004,Laptop Stand,Accessories,138.85
5,SKU-0005,Webcam 1080p,Peripherals,108.15
6,SKU-0006,Noise-cancelling Headphones,Audio,112.03
7,SKU-0007,Desk Lamp,Lighting,92.58
8,SKU-0008,Monitor 27in,Displays,162.89
9,SKU-0009,Ergonomic Chair,Furniture,69.26
10,SKU-0010,Standing Desk,Furniture,355.71
11,SKU-0011,Cable Organiser,Accessories,207.65
12,SKU-0012,Docking Station,Accessories,123.74
13,SKU-0013,Portable SSD,Storage,71.27
14,SKU-0014,Wrist Rest,Accessories,155.22
15,SKU-0015,Bluetooth Speaker,Audio,84.16
16,SKU-0016,Water Bottle,Lifestyle,122.33
17,SKU-0017,Notebook A5,Stationery,363.83
18,SKU-0018,Fountain Pen,Stationery,346.95
19,SKU-0019,Whiteboard,Office,225.67
20,SKU-0020,Desk Mat,Accessories,192.14
Specifications
- Rows
- 20
- Key
- product_key
- Role
- dimension
What is a .csv file?
CSV (Comma-Separated Values) is a plain-text tabular format where rows are lines and fields are separated by commas, with quoting rules for values that contain delimiters, quotes, or newlines. It has no formal type system and depends on encoding and dialect conventions. It is the most portable format for tabular data exchange.
How to use this file
Use an example CSV to test parsers against quoting and embedded-delimiter edge cases, header handling, encoding detection, and import pipelines into databases or spreadsheets.
Code examples
import pandas as pd
df = pd.read_csv("dim_product.csv")
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.
- csvE-commerce Products (CSV, 200 rows)A realistic e-commerce product catalogue (200 rows) — part of a relational dataset (products, customers, orders) with CSV, JSON, SQL, and Parquet twins for testing joins, imports, and conversion.
- 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.
- csvBank Transactions (CSV, 60 rows)A bank-transaction statement — 60 debits and credits across three accounts (masked numbers) with running balances, categories, and merchants. Synthetic data for testing statement parsers, categorisation, and reconciliation.
- jsonBank Transactions (JSON, 60 records)The bank transactions as a JSON array — the format twin of the CSV, for import and reconciliation testing.
Generated by generation/data_edgecases.py. Free for any use, no attribution required — license.