Why

Recklessroosters.com was built with a few things in mind:

• I wanted a light-hearted way to show how seemingly unrelated data can showcase something
• I am a builder, and like building stuff. This keeps me up to date with the latest tech (like fastapi in this case)
• I think Chicken’s really are planning to take over the world. Ever notice how they are all quiet and starts to chirp at the same time? Planning, I say.

How

• In short, RecklessRoosters uses data from the GBIF database for daily animal spottings. We enrich this data, with the distance from a public road using the OSM overpass API. By this process, we can pinpoint any animals that may be jaywalking.
• The GBIF database is a public BigQuery dataset
• The extraction and enrichment process sits in GCP Cloud run
• The source dataset is written back to BigQuery, and displayed on a Looker Studio report
• The website is hosted on GCP App Engine
• The code to the frontend and backend code is available here:
  • https://github.com/sjvdm/recklessrooster-frontend
  • https://github.com/sjvdm/recklessrooster-backend

Todo

• Automate new record sync from GBIF. This dataset is huge though, so think carefully about how to approach resources
• Automate the ELT/OSM overpass backend query to GCP cloud run or AWS lambda. GCP Dataflow seemed like overkill for this use case.
  • Batch process this. In big data - remember Idempotency, scalability, simplicity

More questions?