Project Overview

End-to-end Google Cloud pipeline: a Cloud Run Job (Python) fetches yesterday’s KEF departures from kefairport.is/flug/brottfarir, cleans the data, standardizes timestamps, and appends to a date-partitioned BigQuery table every morning at 06:00 Europe/Zurich. An enriched BigQuery view derives delay and cancellation metrics that power the dashboard below.


Pipeline & Data Model

How the pipeline runs daily

KEF Airport API /api/flightData Cloud Run Job (Python) BigQuery flights_history BQ View v_flights_enriched Looker Studio Dashboard Cloud Scheduler 06:00 daily

How punctuality & delays are computed

BigQuery view for KEF flights enriched KPIs
BigQuery view definition showing time normalization, cancellation logic, delay calculation, and ≤16-minute punctuality rule.

Live Dashboard

Interact with the embedded Looker Studio report. For full-screen view, open it in a new tab.

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Stack & Data Sources

Stack: Google Cloud Run (Jobs), Cloud Scheduler, BigQuery (partitioned), Python (requests, pandas, numpy, google-cloud-bigquery, pyarrow), Looker Studio.

Source: KEF Airport API (/api/flightData) with rolling “yesterday” parameter; enriched KPIs in a BigQuery view.

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