customers
7,795 rows
- idtext · key
- nametext
- personatext
- store_idtext
- created_attime
- last_order_attime
A hose dataset · canonical
The jaffle shop didn't close when the tutorial ended.
dbt's demo shop as a going concern, with a fresh order most minutes the shop is open.
797,284 rows · last row 3 minutes ago
The receipt
- seed
- 42
- scale
- 100
- start
- 2023-09-01
- fingerprint
- 4bb927977c12
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
Schema · 7 tables
orders
251,715 rows
- idtext · key
- customer_idtext
- store_idtext
- ordered_attime
- subtotalreal
- tax_paidreal
- totalreal
items
447,633 rows
- idtext · key
- order_idtext
- skutext
- nametext
- pricereal
tweets
90,060 rows
- idtext · key
- customer_idtext
- order_idtext
- tweeted_attime
- contenttext
stores
6 rows
- idtext · key
- nametext
- opened_attime
- tax_ratereal
products
10 rows
- skutext · key
- nametext
- typetext
- pricereal
- descriptiontext
supplies
65 rows
- idtext · key
- supply_idtext
- nametext
- costreal
- perishablebool
- skutext
Why this one
The jaffle shop from the dbt tutorial is a fixture: a handful of CSVs with a fixed date range and an ending. It does its teaching job and stops.
This one keeps trading. A few regulars carry most of the revenue while the long tail visits once. The seasonality is strong enough to survive a group-by. Because every run with the same seed produces identical bytes, the models you build against it today reconcile against it tomorrow. And because the shop keeps business hours, even the quiet stretches are honest: nobody buys a jaffle at four in the morning.
The other datasets