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