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Scaling pipeline to accept Dynamic inputs
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Azure Solution for SmartBuildings
RAG
Make it variables and reusable
Scaling with branches
Create a Jenkins pipeline named parameter-pipeline that accepts user inputs through parameters to control a deployment configuration. The pipeline should allow the user to specify a custom deployment name, choose the target AWS Availability Zone, and confirm the deployment before execution.
The purpose of this part is to make your Jenkins pipelines interactive, flexible, and production-ready.
So far, your pipelines were:
Static
Hardcoded
Same behavior for every run
In real-world DevOps:
Deployments vary by environment (Dev, UAT, Prod)
Infrastructure changes by region/AZ
Releases need manual confirmation or approvals
Parameterized pipelines solve this by:
Allowing user input during execution
Enabling controlled deployments
Reducing the need to modify pipeline code
This is a critical DevOps practice used in:
Multi-environment deployments
Release approvals
Dynamic infrastructure provisioning
Before starting this part, ensure:
Host and the directory structure to run Dockerfiles and docker-compose.yml (Refer to Part 1)Here is the code that will be used
pipeline {
agent any
parameters {
string(
name: "deploymentName",
defaultValue: "",
description: "Deployment Name?"
)
choice(
name: "azDeploy",
choices: ["ap-southeast-1a", "ap-southeast-1b", "ap-southeast-1c"],
description: "What AZ?"
)
booleanParam(
name: "confirmDeploy",
defaultValue: false,
description: "CONFIRM DEPLOYMENT?"
)
}
stages {
stage("Deploy") {
steps {
echo "Deployment Name: ${params.deploymentName}"
echo "AZ Selected: ${params.azDeploy}"
echo "Deployment Confirmation: ${params.confirmDeploy}"
}
}
}
}
parameter-pipeline"
pipeline script
build with parameters
console output
Done!!!
In this part, you transformed your pipeline into a dynamic, user-driven CI/CD system
We learned how to:
Accept user inputs directly from Jenkins UI
Control pipeline execution using parameters
Make deployments flexible and environment-aware
Reduce hardcoding in pipeline scripts
This is how real-world pipelines operate in production:
Same pipeline, multiple use cases
Controlled deployments with user validation
Better flexibility without changing code
⬅️ Previous Article: Part 8 Multibranch Pipelines
➡️ Next Article: Part 10 Environment Variables in Pipelines
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