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Transat Watcher

Transat Watcher email alert preview

A Python automation tool that monitors Air Transat vacation package prices and sends instant email alerts when they change — plus a daily morning summary of all watched hotels. Runs completely free on GitLab CI scheduled pipelines.

Challenge & Solution

Tracking vacation prices manually is tedious — you have to remember to check, and by the time you do, the deal may already be gone. Air Transat's site has no built-in price alerts, so I built one myself.

The watcher handles the whole problem end-to-end:

  • Automated Hourly Checks: Polls Air Transat's public API every hour via a scheduled GitLab CI pipeline — no server to maintain and no cost.
  • Instant Price Alerts: Sends an email the moment any watched hotel's price drops or rises, with the exact amount and direction of the change.
  • Daily Morning Summary: Delivers a digest email each morning with current prices across all watched hotels, so you stay informed without needing to check the site.
  • Multi-Hotel Support: Watch as many hotels and room types simultaneously as you want — just add entries to a simple config list.
  • Price History Database: SQLite stores every price check locally, enabling comparisons and trend analysis over time.

Tech Stack:

Python GitLab CI

Key Features

Built to run silently in the background and only bother you when something actually changes.

Instant Price Alerts

Email notifications fire the moment a price changes — up or down — including the exact dollar amount so you know whether it's worth acting on.

Daily Morning Digest

A scheduled summary email lands in your inbox each morning with a clean table of all watched hotels and their current prices.

Free GitLab CI Deployment

Runs on GitLab's free scheduled pipeline tier — no server, no hosting fees, no infrastructure to manage. Push and schedule once, forget about it.

SQLite Price History

Every price check is persisted in a local SQLite database, enabling historical comparisons and making it easy to spot pricing trends over time.

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