Billing

  • All resources should include labels for better classification and cost attribution. The labels should include but not be limited to the following:
    • Org - the Syensqo organization/GBU that "owns" the resource
    • Project - the project/initiative that the resource supports
    • App - the application/solution that the resource supports
  • All GCP projects should have budgets defined and alert notifications set on those budgets.
    • The budget set for the project should be the median consumption cost of the project over the last 12 months, or the target budget estimated by the project/initiative team.
    • Alerts should be set for the 50%, 75%, and 90% or budget thresholds.
  • Billing data should be automatically exported to a designated BigQuery table so it can be shared with the appropriate members of management for reporting and cost optimization purposes.


Metrics

The following (non-exhaustive) list of metrics should be captured:

  • Files uploaded to Cloud Storage buckets
  • Files deleted from Cloud Storage buckets
  • Number of rows in each BigQuery/CloudSQL table
  • Size of data stored in each BigQuery/CloudSQL table
  • Execution time of data processing jobs
  • Number of successful data processing job executions
  • Number of failed data processing job executions
  • Number of successful code builds
  • Number of failed code builds


KPIs

The following (non-exhaustive) list of KPIs should be calculated and available to include/visualize in reports:

  • Number of files uploaded to Cloud Storage per day
  • Number of files uploaded to Cloud Storage per day per lab
  • Number of files deleted Cloud Storage per day
  • Average execution time of all data processing jobs
  • 95% execution time of all data processing jobs
  • Average execution time of each data processing job
  • 95% execution time of each data processing job
  • Number of successful code builds per day
  • Number of failed code builds per day
  • Percent of successful code builds over all code build attempts, per day
  • Percent of successful code builds over all code build attempts, per user
  • Percent of successful code builds over all code build attempts, per user, per week
  • Percent of failed code builds over all code build attempts, per user
  • Percent of failed code builds over all code build attempts, per user, per week


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