Aviators & Google Cloud Developer Partnership

Building Aviator from the ground up on Google Cloud was an obvious choice

This allowed Google team to investigate a number of cloud possibilities without having to worry too much about price

Automated code review guidelines, real-time reviewer input, and predetermined response time targets enhance code review cycles

Stack pull requests (PRs), which are modest code changes that can be independently reviewed in a predetermined order

Verifying isolated code changes before merging them into the main line of development increases deployments and reduces change failures

Development teams may provide more dependable products and systems and shorten the time it takes to recover from production failures

Google used Managed Service for Prometheus to monitor and notify Aviator without worrying about scalability or dependability

Aviator uses API calls as a main method of communication with external services like GitHub, PagerDuty, and Slack

In order to identify sluggish queries on Aviator, They have been investigating query labelling with Sqlcommenter more recently

Additionally, They make use of the Python module Sqlcommenter, which works nicely with the backend of our application

Google upload new versions of the Aviator programme as Docker images to Google Cloud’s Artefact registry and publish Helm charts to a private repository