Why Jenkins: CI/CD for data pipelines—validate before deploy; automate releases. Architectural logic: Pipeline as Code (Jenkinsfile): lint, unit test, integration test, deploy. Build: package code, Docker images. Test: pytest, dbt test. Deploy: push artifacts, update Airflow DAGs. Integrations: Git webhooks, S3/Nexus, deployment targets. Scalability: Jenkins is single point; master/agent for parallel. Trade-offs: Jenkins is powerful but heavy; GitHub Actions/GitLab CI may be simpler....
The complete answer continues with detailed implementation patterns, architectural trade-offs, and production-grade considerations. It covers performance optimization strategies, common pitfalls to avoid, and real-world examples from companies like Coforge. The answer also includes follow-up discussion points that interviewers commonly explore.
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