My results after moving from Jenkins to GitHub Actions - build times dropped 40%

The perennial debate between dedicated CI/CD platforms and orchestrators often centers on flexibility versus specialization. As someone who typically operates in the data pipeline domain, my team's build and test processes for our data ingestion frameworks (Airbyte, dbt) were historically managed within Jenkins. While functional, the maintenance overhead and performance characteristics prompted a rigorous evaluation and subsequent migration to GitHub Actions. The headline result—a 40% reduction in median build time—was a significant, though not the sole, benefit. This post details the technical migration approach, the cutover strategy, and a retrospective on the timeline.

Our Jenkins pipeline was declarative and had evolved to handle several key workflows:
* Multi-branch pipeline builds for feature branches.
* Integration testing against a live BigQuery sandbox.
* Docker image builds for custom Airbyte connectors.
* Deployment of our dbt documentation site.

The primary pain points were queue times on our self-managed agents, configuration sprawl across numerous `Jenkinsfile` scripts, and the cognitive load of managing the Jenkins server itself. The migration strategy was a phased, workflow-by-workflow approach, prioritizing the most time-consuming pipelines first to realize speed gains early.

**Migration Approach & Configuration Paradigm Shift**

The core of the migration involved translating Jenkins pipeline stages into GitHub Actions jobs and steps. We embraced Actions' composability through reusable workflows and custom composite actions. A critical design decision was to leverage the GitHub Actions matrix strategy for parallelizing our integration test suites across multiple Python versions, which was cumbersome in our previous setup.

Below is a comparative example of a simplified dbt test runner.

**Jenkinsfile (Declarative Snippet)**
```groovy
pipeline {
agent any
stages {
stage('Test dbt Project') {
steps {
sh '''
cd analytics/dbt
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
dbt test --target ci
'''
}
}
}
}
```

**Equivalent GitHub Actions Workflow (.github/workflows/test_dbt.yml)**
```yaml
name: Test dbt
on: [push]
jobs:
dbt-test:
runs-on: ubuntu-latest
env:
DBT_PROFILES_DIR: ./analytics/dbt
steps:
- uses: actions/checkout@v4
- uses: actions/setup-python@v5
with:
python-version: '3.10'
cache: 'pip'
- run: pip install -r analytics/dbt/requirements.txt
- run: dbt deps
working-directory: ./analytics/dbt
- run: dbt test --target ci
working-directory: ./analytics/dbt
env:
BIGQUERY_SERVICE_ACCOUNT: ${{ secrets.BIGQUERY_CI_SERVICE_ACCOUNT }}
```

The key improvements here are native caching support for Python dependencies, automatic secret management via GitHub Secrets, and the pre-configured, maintained runner environment. We systematically decomposed each pipeline in this manner.

**Cutover Plan & Performance Analysis**

We executed a shadow run period of two weeks. During this time, both systems ran in parallel for all pushes to the main branch, but only Jenkins deployments were production-bound. This allowed us to compare build times directly and validate artifact outputs. The cutover was performed on a per-repository basis during a scheduled maintenance window, flipping the deployment trigger from Jenkins to GitHub Actions.

The performance improvement was immediately measurable. The 40% reduction in median build time was attributable to several factors:
* **Reduced queue time:** Elimination of contention on our self-managed agents.
* **Superior caching:** GitHub Actions' `cache` and `actions/setup-*` actions proved more efficient than our manual Jenkins caching logic.
* **Faster artifact uploads/downloads:** Tight integration with GitHub's infrastructure.
* **Parallelization:** Easier implementation of matrix builds for concurrent testing.

**Timeline & Retrospective**

The total transition took approximately six calendar weeks from initial planning to full decommissioning of Jenkins for our core data projects.
* **Week 1-2:** Inventory of all Jenkins pipelines and design of reusable Actions components.
* **Week 3-5:** Incremental migration and shadow running of key pipelines.
* **Week 6:** Cutover, monitoring, and Jenkins agent deprovisioning.

The transition has yielded unexpected benefits beyond speed. The proximity of CI configuration to the code in `.github/workflows` has improved collaboration with our analytics engineers. Furthermore, the built-in artifact storage and logs have simplified debugging. For data teams operating in a modern stack (Airbyte, dbt, BigQuery), the move to a tightly integrated CI/CD platform like GitHub Actions can reduce significant operational toil, allowing more focus on pipeline logic and data quality itself.