hose datasets Demo data with a pulse

A hose dataset · canonical

Northwind is still taking orders.

The 1996 wholesale catalogue, regenerated as a going concern with orders through yesterday.

SQLite one file, ready to query CSV tar.gz, one file per table

233,940 rows · last row 5 minutes ago

hose → northwind 233,940 rows live
Rows as the generator writes them, straight from the API. Real speed. Quiet hours are quiet, the way a real business is.

The receipt

seed
42
scale
450
start
2022-01-01
fingerprint
60e583260455

The whole dataset grows from this one seed. Everyone who downloads it gets the same rows with the same ids, today and next month, so whatever you build on it keeps working after the data refreshes.

Orders per day, last 90 days

2026-06-18: 142026-06-19: 112026-06-20: 1682026-06-21: 1702026-06-22: 112026-06-23: 92026-06-24: 152026-06-25: 112026-06-26: 112026-06-27: 1602026-06-28: 1802026-06-29: 122026-06-30: 72026-07-01: 122026-07-02: 62026-07-03: 102026-07-04: 1942026-07-05: 1682026-07-06: 72026-07-07: 152026-07-08: 112026-07-09: 162026-07-10: 142026-07-11: 1612026-07-12: 1702026-07-13: 122026-07-14: 82026-07-15: 142026-07-16: 122026-07-17: 62026-07-18: 1722026-07-19: 1572026-07-20: 92026-07-21: 192026-07-22: 112026-07-23: 152026-07-24: 62026-07-25: 1632026-07-26: 1532026-07-27: 92026-07-28: 72026-07-29: 152026-07-30: 122026-07-31: 72026-08-01: 1752026-08-02: 1492026-08-03: 132026-08-04: 122026-08-05: 72026-08-06: 152026-08-07: 122026-08-08: 1582026-08-09: 1682026-08-10: 102026-08-11: 82026-08-12: 122026-08-13: 152026-08-14: 112026-08-15: 1212026-08-16: 1622026-08-17: 102026-08-18: 132026-08-19: 122026-08-20: 172026-08-21: 152026-08-22: 1552026-08-23: 1562026-08-24: 82026-08-25: 102026-08-26: 112026-08-27: 212026-08-28: 122026-08-29: 1832026-08-30: 1822026-08-31: 122026-09-01: 192026-09-02: 142026-09-03: 172026-09-04: 122026-09-05: 1592026-09-06: 1712026-09-07: 192026-09-08: 192026-09-09: 112026-09-10: 142026-09-11: 102026-09-12: 1822026-09-13: 1742026-09-14: 192026-09-15: 7

Schema · 13 tables

Categories

8 rows

  • CategoryIDint · key
  • CategoryNametext
  • Descriptiontext
  • Picturetext

CustomerDemographics

0 rows

  • CustomerTypeIDtext · key
  • CustomerDesctext

Customers

1,963 rows

  • CustomerIDtext · key
  • CompanyNametext
  • ContactNametext
  • ContactTitletext
  • Addresstext
  • Citytext
  • Regiontext
  • PostalCodetext
  • Countrytext
  • Phonetext
  • Faxtext

CustomerCustomerDemo

0 rows

  • CustomerIDtext · key
  • CustomerTypeIDtext

Employees

8 rows

  • EmployeeIDint · key
  • LastNametext
  • FirstNametext
  • Titletext
  • TitleOfCourtesytext
  • BirthDatetime
  • HireDatetime
  • Addresstext
  • Citytext
  • Regiontext
  • PostalCodetext
  • Countrytext
  • HomePhonetext
  • Extensiontext
  • Phototext
  • Notestext
  • ReportsToint
  • PhotoPathtext

EmployeeTerritories

10 rows

  • EmployeeIDint
  • TerritoryIDtext · key

Order Details

177,721 rows

  • idtext · key
  • OrderIDint
  • ProductIDint
  • UnitPricereal
  • Quantityint
  • Discountreal

Orders

54,108 rows

  • OrderIDint · key
  • CustomerIDtext
  • EmployeeIDint
  • OrderDatetime
  • RequiredDatetime
  • ShippedDatetime
  • ShipViaint
  • Freightreal
  • ShipNametext
  • ShipAddresstext
  • ShipCitytext
  • ShipRegiontext
  • ShipPostalCodetext
  • ShipCountrytext

Region

4 rows

  • RegionIDint · key
  • RegionDescriptiontext

Products

80 rows

  • ProductIDint · key
  • ProductNametext
  • SupplierIDint
  • CategoryIDint
  • QuantityPerUnittext
  • UnitPricereal
  • UnitsInStockint
  • UnitsOnOrderint
  • ReorderLevelint
  • Discontinuedbool

Shippers

4 rows

  • ShipperIDint · key
  • CompanyNametext
  • Phonetext

Suppliers

24 rows

  • SupplierIDint · key
  • CompanyNametext
  • ContactNametext
  • ContactTitletext
  • Addresstext
  • Citytext
  • Regiontext
  • PostalCodetext
  • Countrytext
  • Phonetext
  • Faxtext
  • HomePagetext

Territories

10 rows

  • TerritoryIDtext · key
  • TerritoryDescriptiontext
  • RegionIDint

Why this one

Northwind has taught databases since 1996, and its dates say so. The freshest order in the classic file is older than some of the people studying it.

This version runs the same wholesale business forward. Customers reorder on their own rhythms. Freight varies by carrier and by weight, and a believable share of deliveries runs late. Prices move over the years, so an invoice from 2022 reflects 2022. The seed is fixed and the bytes reproduce, yet yesterday’s orders are in the file you download today.

The other datasets