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Public sample databases. Their dates are moved forward so questions about recent periods work: the dates in the report are not the dates of the original data (the rental shop’s data is from 2022).

Airline (sample)

PostgreSQL · Measured Oct 14, 2026

Read-only: STYD never changes your database.

9 tables, ≈ 10.7M records, 4 business areas. Here’s what you can ask and build with it.

This database records details about airline bookings, flights, passengers, and related information. It keeps track of tickets, flight segments, boarding passes, flight schedules, airports, airplanes, and seating arrangements. It seems to capture the full scope of airline travel data from booking to boarding.

How it works

  1. books
  2. boards
  3. flies

What stands out

  • Some tables contain dates in the future (up to December 2026): flights.

Questions you can ask

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Ideas you could build

  • Flight Activity Dashboardreport

    A dashboard showing current flight statuses, active flights, and booking volumes.

  • Passenger Travel Summaryreport

    A report summarizing passenger ticket details, segments traveled, and boarding passes issued.

  • Booking Alert Systemautomation

    An automated alert that notifies when bookings exceed a certain volume or when flights are fully booked.

  • Seat Availability Reportreport

    A regular report showing available seats by airplane and fare conditions.

Tables

9

Records in total

≈ 10.7M

Oldest record

Aug 2026

Latest activity

—

What it can tell you

  • Which flights are currently active and their status.
  • How many bookings and tickets have been made and their total amounts.
  • Which passengers have tickets and their travel details including segments and boarding passes.
  • Details about flight routes, airports, and airplane models used.
  • Seat availability and fare conditions on airplanes.

Want something built on this data?

Is it safe?

  • Read-only: connect with a read-only database user. STYD only runs read queries and never changes your data.
  • What is stored: table and column names, how many records each table has, its first and last dates, how the tables link, and sample values from your columns (none from columns detected as email addresses, phone numbers or secrets). To understand the data, the analysis reads a limited sample of rows from each table.
  • What the AI sees: the written explanation is made from table and column names and a rough size of each table, not from your rows. To draft column descriptions, the analysis sends the AI a few example values per column, with long numbers partly masked.
  • Every number on the report is counted by the database itself, not written by the AI.
  • PostgreSQL and MySQL.
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