📄️ Fundamentals of Delivery Strategies
In modern change management, the key task is to minimize risks when deploying new features while ensuring system stability. Deployment methods such as Big Bang, Phased Rollouts, Dark Launches, Blue-Green Deployment, and A/B Testing allow for a flexible approach to implementing changes depending on the context and business goals.
📄️ Phased Rollouts
Phased Rollouts, or phased deployment, is a general term that describes strategies for implementing changes through gradual scaling. In this approach, new features or updates are introduced to specific groups of users or environments to minimize risks and manage workload effectively.
📄️ Dark Launches
Dark Launches are a method of deploying new features into production while keeping them hidden from most users until the official release. The main goal is to deploy the functionality in a real-world environment to test its performance, stability, and integration with existing systems without impacting end users.
📄️ Blue-Green
Blue-Green Deployment is a deployment and change management strategy in which two identical environments are maintained simultaneously: Blue (current production environment) and Green (new application version). Users interact with the Blue environment, while the Green environment is prepared for updates. After successful testing and verification, traffic is switched to the Green environment, which then becomes the new primary production environment.
📄️ Big Bang
Big Bang Deployment is a deployment strategy where all changes are implemented simultaneously in the target environment. Unlike Phased rollouts or Dark Launches, Big Bang Deployment involves making all new functionalities available to all users at once. This approach carries a high level of risk, as all changes become active simultaneously, and rolling back changes is typically more challenging.
📄️ A/B Testing
A/B testing is a method of comparing two or more versions of a feature to evaluate their effectiveness. The goal of A/B testing is to determine which version better achieves set objectives, such as performance, user experience, or conversion rates. It also helps identify factors that influence user behavior.