DevOps and Continuous Delivery
Last updated
Last updated
Cloud-native principles form the foundation for agile methods, DevOps, and continuous improvement. Supported by DevOps practices like automation and collaboration, and continuous delivery pipelines, cloud-native principles empower teams to adapt quickly, reduce risk, and consistently deliver value.
DevOps provides the cultural framework, while Continuous Delivery (CD) delivers the technical practices for implementing cloud-native systems.
DevOps is a cultural and organizational approach that eliminates silos between development, operations, and testing teams to foster seamless collaboration. A personal example highlights the importance of this:
"Years ago, I worked on a development team for a major airline, building a tool using new technology—a NoSQL database. Despite our progress, the operations team eventually refused to support it because their processes only allowed relational databases. We had to redesign the architecture to meet operational requirements, wasting time and effort" Dirk Slama, Lead Author
This story underscores the core principle of DevOps: ensuring alignment between different teams like development and operations from the start.
CD automates the software release process, enabling frequent and reliable deployments. Tools like Jenkins, Docker, and Selenium streamline this pipeline, making it possible to:
Commit code.
Build systems.
Test, deploy, and operate systems efficiently.
The synergy between DevOps and CD results in faster delivery, reduced errors, and continuous feedback, essential for innovation at scale.
The DevOps infinity loop illustrates the lifecycle of software delivery:
Plan: Define upcoming changes.
Develop: Write and test code or create AI models.
Build: Compile and package source code or AI models into executable software that is ready for deployment.
Test: Validate functionality and performance.
Release: Deploy software (including AI models) to production.
Operate: Monitor the system in real-time.
Learn: Gather insights to improve future iterations.
Modern development extends beyond traditional coding to include training and fine-tuning AI models, integrating data from operational environments, and deploying assets not just to the cloud but also to edge devices, such as smartphones and IoT devices.
Pipelines automate and streamline the development lifecycle, from code commitment to deployment. Key tools include:
GitHub: Code management.
Maven: Build automation.
Selenium: Web testing.
Docker: Containerization.
Jenkins: Orchestration and automation across all steps.
These pipelines ensure faster delivery, higher quality, and reduced errors. However, in the automotive industry—especially for embedded systems—many of these goals remain unmet.
Consider an example where two teams develop components independently:
Team A develops a smartphone app.
Team B builds a cloud backend for the app.
Each team uses its own pipeline to independently test and iterate on its component. During early stages:
Team A uses a mock backend service for testing.
Team B uses a mock smartphone app for validation.
Once both components are stable, they merge in an integration pipeline to:
Test the combined system.
Deploy the complete solution.
Monitor the system holistically to identify improvements.
This approach ensures flexibility in development while enabling smooth integration and deployment.
While these cloud-native principles are widely adopted in internet-based systems, the automotive industry—especially for embedded software—faces significant challenges. Automation, agile practices, and continuous delivery pipelines offer immense potential to transform this sector by enabling:
Faster delivery.
Enhanced quality.
Continuous feedback and innovation.
By learning from cloud-native best practices, automotive development can evolve to meet the demands of modern software-defined vehicles.
