Implement Operational Patterns
There are workflows and motions essential to the successful operations of a data science environment. Early in a data science team’s journey, the fundamentals may include establishing good practices around version control.
As teams mature, they may develop or or adopt operational workflows including working with dev-test-prod environments, deploying with CI/CD, auditing and monitoring, and other DevOps functions.
The articles below offer guidance in learning and adopting these common operational workflows that are typically present in enterprise environments.
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Adopting operations in your development environment
Working with Git in Posit Workbench
Git Learning Resources
happygitwithr
Get started using Git with R
Get started using Git with R
Learn Git
Online edition of the text "Pro Git" by Scott Chacon and Ben Straub
Online edition of the text "Pro Git" by Scott Chacon and Ben Straub
Learn Git Branching
An interactive way to learn git
An interactive way to learn git
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Operational patterns in deployment
Code Promotion to Production
Best Practices in Managing Deployed Content
Automation in Production
A discussion on code promotion and implementing this workflow with different server environments
Push-button, Git-backed, or Programmatic to support different workflows
Each page in this section has detailed examples how various CI/CD system can be utilized to deploy content to Connect.
Discussing workflows to incorporate Dev/Test/Prod progressions, peer review, or CI/CD.
An example workflow for management of development and production deployments using git
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Auditing and Monitoring deployed content
Create scheduled reports and dashboards to track and manage the content on their servers.
Utilize Graphite or Prometheus endpoints for metrics and monitoring
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