Star Schema — Sales Fact Table (CSV, 300 rows)
The sales fact table at the centre of a star schema — 300 sale lines with foreign keys to the date, product, and customer dimensions plus quantity and amount measures. A fixture for testing joins, star-schema imports, and BI tools.
sale_id,date_key,product_key,customer_key,quantity,amount
1,20260124,12,20,2,247.48
2,20260119,1,3,3,531.93
3,20260121,20,8,5,960.7
4,20260117,2,15,2,297.06
5,20260119,10,15,4,1422.84
6,20260120,16,38,5,611.65
7,20260102,20,19,1,192.14
8,20260112,18,37,5,1734.75
9,20260119,9,27,2,138.52
10,20260122,13,27,5,356.35
11,20260124,2,27,3,445.59
12,20260105,17,30,1,363.83
13,20260110,18,5,2,693.9
14,20260127,8,7,2,325.78
15,20260128,14,38,1,155.22
16,20260117,11,32,2,415.3
17,20260112,8,27,2,325.78
18,20260109,5,32,2,216.3
19,20260106,7,31,5,462.9
20,20260109,6,2,5,560.15
21,20260102,1,30,5,886.55
22,20260128,1,13,4,709.24
23,20260124,16,26,3,366.99
24,20260111,6,9,2,224.06
25,20260115,9,36,5,346.3
26,20260110,5,40,3,324.45
27,20260104,6,39,1,112.03
28,20260126,5,5,3,324.45
29,20260130,9,12,2,138.52
30,20260115,10,30,5,1778.55
31,20260122,19,1,5,1128.35
32,20260127,2,39,1,148.53
33,20260105,13,7,4,285.08
34,20260103,15,20,2,168.32
35,20260102,11,34,4,830.6
36,20260104,18,25,4,1387.8
37,20260121,8,9,2,325.78
38,20260126,4,25,5,694.25
39,20260122,19,34,3,677.01
40,20260126,17,2,2,727.66
41,20260111,7,27,3,277.74
42,20260111,7,35,1,92.58
43,20260112,16,21,4,489.32
44,20260106,9,29,1,69.26
45,20260116,5,9,5,540.75
46,20260107,17,37,4,1455.32
47,20260125,4,27,1,138.85
48,20260110,11,24,5,1038.25
49,20260118,1,10,2,354.62Specifications
- Rows
- 300
- Grain
- one row per sale line
- Foreign Keys
- date_key, product_key, customer_key
- Role
- fact
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.
How to use this file for testing
“Star Schema — Sales Fact Table (CSV, 300 rows)” is a deterministic Novus Examples fixture for Data import, Data engineering, Conversion testing. Realistic faker-generated datasets with documented schemas for testing import and ETL flows.
Documented properties for this file: fact · 300 rows. 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
df = pd.read_csv("fact_sales.csv")
print(df.head())
print(df.dtypes)Related files
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- 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.