Retail star schema
A dimensional model you can actually practise joins on: one fact table, four dimensions, 63,170 rows, and every join resolving.
Every free mock-data generator hands you one flat CSV, and a flat file has no joins. That makes it useless for the exact skill most people downloading practice data are trying to learn. This is a real star schema: fact_sales joined to date, product, customer and store dimensions, with no orphaned rows and no duplicate keys in any dimension. Two things about it are declared rather than sampled, which is what makes it worth practising on: the category share of revenue is exact to the cent, and monthly revenue follows a stated two-year curve. Your GROUP BY has an answer that is known to be right, so you can check your own SQL instead of eyeballing whether it looks plausible.
- star schema
- dimensional model
- BI practice
- Power BI
- Tableau
- SQL joins
Explore
Every table, profiled
Each column's type, spread, empties and most common values, measured from the CSVs in the download. Switch to the first rows to see the data exactly as it sits in the file.
5 tables
fact_sales.csv
60,000 rows · 11 columns · 4 foreign keys
- Electronics42%
- Home26%
- Apparel20%
- Grocery12%
Keys
Every join resolves
4 foreign keys, 240,000 references checked against the table each one points at. None points at a row that does not exist.
fact_sales
- date_keydim_date.date_key60,000 rows checked0 orphans
- product_keydim_product.product_key60,000 rows checked0 orphans
- customer_keydim_customer.customer_key60,000 rows checked0 orphans
- store_keydim_store.store_key60,000 rows checked0 orphans
Checks
What we checked
- Zero orphaned rows joining fact_sales to all four dimensions
- Zero duplicate keys in dim_date, dim_product, dim_customer and dim_store
- Electronics is 42.00% of revenue, Home 26.00%, Apparel 20.00%, Grocery 12.00%
- January 2024 revenue is exactly 820,000.00, as declared
- December 2024 is exactly 1,150,000.00 and December 2025 exactly 1,480,000.00
- Checked with DuckDB against these exact files, which shares no code with the generator
The same checks ship in the zip as INTEGRITY.txt, so you can rerun them in any SQL engine. How we verify
Questions
Worth asking it
- 1Which category earns most in which region?
- 2How does revenue grow month over month across two years?
- 3Do premium products sell better to Corporate or to Consumer?
- 4Which stores beat their region's average basket size?
Full version · $15
This sample, or the full Retail Data Warehouse
Two fiscal years of a grocery chain, from the shelf to the receipt. Try its free preview before you decide.
| This sample | Retail Data Warehouse | |
|---|---|---|
| Rows | 63,170 | 2,790,852 |
| Tables | 5 | 14 |
| Columns | 33 | 194 |
| Formats | CSV | CSV, Parquet, SQL DDL, worked SQL |
| Audit | Keys and the checks listed above | 117 checks, all listed |
| Licence | CC0, free | Commercial use, $15 once |
| Built for | star schema, dimensional model, BI practice, Power BI | Star schema and BI, Demand forecasting, Promotion and price analytics, Inventory and supply chain, Customer analytics, Returns and fraud-style exercises |
Use it
Take it, or make one shaped like yours
Rebuild it byte for byte
The zip includes schema.yaml. Run misata generate --config schema.yaml with seed 21 and you get the same rows.
Your own tables, your own numbers
Describe the data you need in Studio and get connected tables with the totals you state, profiled like this and checked before you download.
The full version, and what goes with it
All premiumWeekly sales, at regular price and on promotion
Grocery chain, 10 stores, fiscal 2024 and 2025
Two fiscal years of a grocery chain, from the shelf to the receipt
Answer key
- baseline_units
- price_effect
- promo_lift
- +7 more
- Rows
- 2,790,852
- Tables
- 14
- Checks
- 117 of 117
Free previewProfile and previewAn X-bar chart with the truth underneath: flatness, characteristic 139
3 plants, 36 CNC machines, six months
Six months of control charts with the ground truth underneath
Answer key
- active_cause_id
- true_cause_shift_sigma
- true_tool_wear_sigma
- +6 more
- Rows
- 515,442
- Tables
- 15
- Checks
- 101 of 101
Free previewProfile and previewVibration through a life that ends in failure
240 machines at 4 plants, two years
Two years of sensor readings, failures, repairs and costs for 240 machines
Answer key
- failure_mode
- true_life_days
- true_failure_at
- +6 more
- Rows
- 179,038
- Tables
- 6
- Checks
- 67 of 67
Free previewProfile and preview
Related free datasets
All 12- Ecommerce storefrontA year of orders across five joined tables, with a real Q4 peak and totals that reconcile to the cent.49,192 rows · 5 tables
- Machine degradation100 machines monitored from commissioning to failure, with an exact remaining-useful-life label on every one of 23,118 readings.46,336 rows · 3 tables
- B2B SaaS subscription analyticsAccounts, seats, MRR, churn and support load, where company size actually drives the plan.44,384 rows · 5 tables
- Credit risk portfolio3,000 borrowers and 4,200 commercial loans, where a credit rating actually predicts whether the loan defaults, at the rate S&P's own data says it should.7,200 rows · 2 tables
- Network intrusion / netflow500 hosts and 20,000 labeled flows, where a port scan, a DDoS flood, and a brute-force attempt each carry the real statistical signature their MITRE ATT&CK technique actually leaves in traffic.20,500 rows · 2 tables
- Contact center queueing (Erlang C)4 queues, 2,400 half-hour intervals over a full business month, where agents_staffed is what Erlang C (1917) says is actually required to clear each queue's own SLA target, not a plausible-looking number.2,404 rows · 2 tables
Fully synthetic. No real person, company or transaction is represented, and no production data was read to make it. CC0: use it anywhere, no attribution needed.