DVD rental

App gallery / Business

DVD rental: total amount by paid at, by year, new film form, film record

Shown in Cream Masthead Ember · 16 featured looks, 520+ more to choose from

DVD rental

A demo business app built on Pagila, the PostgreSQL version of the Sakila sample database: 1,000 films with their actors, categories and languages, the copies each store holds, staff, customers and their addresses, and about 52,000 rentals with 51,000 payments from 2022 to 2026. Every person, store and sale is fictional. Use it to see how a full rental business app works with realistic data before you build your own.

Database app · 15 tables · design: Cream Masthead Ember

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Tables

  • countries3 columns · 109 rows in the full data
  • cities4 columns · 600 rows in the full data
  • addresses7 columns · 1,003 rows in the full data
  • languages3 columns · 6 rows in the full data
  • categories3 columns · 16 rows in the full data
  • actors4 columns · 200 rows in the full data
  • films13 columns · 1,000 rows in the full data
  • film_actors3 columns · 5,462 rows in the full data
  • film_categories3 columns · 2,367 rows in the full data
  • stores4 columns · 500 rows in the full data
  • staff7 columns · 1,500 rows in the full data
  • customers8 columns · 999 rows in the full data
  • inventory4 columns · 4,581 rows in the full data
  • rentals7 columns · 51,805 rows in the full data
  • payments7 columns · 51,061 rows in the full data

Sources and licences

  • Pagila — PostgreSQL Licence (permissive) (Free to use)

Shown in the app and its export: Sample data from Pagila by Devrim Gündüz (PostgreSQL Licence), a port of the Sakila sample database.

Sample: the whole source. Full data: 121,209 rows, 13.1 MB. Measured 2026-10-22.

Data from github.com, imported 2026-10-22.

The full data, for your own database

121,209 rows, 2.0 MB to download. CreativePower keeps only the made-up sample rows in your project; load the full data into your own PostgreSQL database (Supabase works): download these files into one folder, unzip the .csv.gz files, then run psql "<your connection string>" -f load.sql.