The 8 Phases of Continuous Integration & Continuous Delivery

Learn best practices for structuring your CI/CD pipeline

Discover the key principles for dramatically improving DevOps continuous integration and continuous delivery (CI/CD) processes. This complimentary guide reveals an 8-phase framework for successful CI/CD pipelines.

This CI/CD framework guide provides:

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Industry-Validated Best Practices for Implementing CI/CD

To create this guide, Densify compiled information from experts and businesses in the public cloud and cloud native architectures space who have implemented CI/CD as part of their DevOps strategy. All identified similar challenges which are revealed in the guide.

Ultimately, we identified 8 common phases to successful CI/CD pipelines:

  1. The Plan phase often combines practices from Scrum and Agile to enable frequent microincremental releases.
  2. The Code phase focuses on core development tasks from within IDEs and appropriate sandboxing and frameworks.
  3. The Build phase rapidly and incrementally merges code commits with some testing and security validation.
  4. The Test phase focuses on automated verification of enhancements, often incorporating test-driven deployment practices. Testing is sometimes incorporated as part of the Build phase, and generally extends in some way to all phases of the CI/CD process to ensure continuous feedback and improvement.
  5. The Release phase is centered around repository commits and adequate documentation of the changes.
  6. The Deploy phase is the actual update to the codebase, with special thought given to issue and error avoidance.
  7. The Operate phase occurs once the code is made live, and consists of monitoring and orchestration.
  8. The Monitor & Optimize phase takes place parallel to the Operate phase and consists of data collection, analysis, and feedback to the start of the pipeline and to other phases as needed. In the most sophisticated environments, optimization is automated as continuous optimization (CO), an extension of CI/CD that leverages machine learning to eliminate risks and waste associated with manual infrastructure selection.

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