The goal of a DevOps pipeline is to create a continuous workflow that includes the entire application lifecycle. But too often, people focus only on the tools and automating everything, not stopping to think whether their processes could further improve performance and efficiency. Let's look at some common challenges to continuous delivery and then learn five tips for refining your DevOps pipeline and taking it to the next level.
DevOps is both a software development methodology and a mindset. As a methodology, it’s about unifying development and operations into one team. As a mindset, DevOps is about collaboration, communication, and information sharing.
At the heart of the DevOps approach is the application delivery pipeline. The application delivery pipeline drives the development and deployment processes forward throughout the application lifecycle.
Let’s review some of the basic concepts of DevOps, including the stages of a DevOps pipeline, as well as some tips that you can use to reach beyond the basics with your DevOps pipeline.
Introduction to the DevOps Pipeline
DevOps is a development approach that unifies the application delivery process into a continuous flow. The continuous processes include planning, development, testing, operations, and deployment. The ultimate goal of DevOps is to deliver applications more easily and quickly.
A DevOps pipeline is usually represented by six distinct stages: commit, build, test, deploy, release, and operate. Each company can represent the pipeline differently, but the goal is the same—to create a continuous workflow that includes the entire application lifecycle.
Why Businesses Need a Strong CI/CD Pipeline
A strong CI/CD pipeline enables you to deploy on demand without sacrificing product quality, security, or functionality. It can increase DevOps productivity and speed development processes significantly.
Here are a few more benefits organizations can gain from well-deployed pipelines:
- Greater visibility: Pipelines can offer increased visibility into processes, enabling teams to identify and correct bottlenecks and other issues. Visibility also helps encourage accountability, so teams can work together and communicate more effectively.
- Faster feedback: Pipelines enable teams to return feedback to members as soon as possible through automated and prioritized testing. This helps ensure that issues are fixed quickly and keeps all members aware of project status.
- Shorter time to value: Pipelines facilitate continuous improvement and deployment of code. As soon as value is added to a product, it is delivered to customers, making teams more productive and products more competitive.
3 Common Challenges Slowing Down Continuous Delivery
Despite the benefits that pipelines can provide, there are several common challenges that teams face when using these tools for continuous delivery. Three major and common challenges are tight deadlines, poor communication, and careless automation.
1. Tight deadlines and release schedules
Pipelines can be useful tools for ensuring steady and efficient flow of work, but proper implementation takes planning. These tools cannot magically cause teams to meet unrealistically tight schedules, especially when first implemented.
To benefit from pipelines, teams need to set schedules that reflect the actual amount of work that can be performed without sacrificing quality. This means adjusting timelines based on measurable performance improvements. It also means implementing procedures to both collect metrics and hold team members accountable for inconsistencies.
Once your pipelines are in place, make sure to verify that processes are more efficient and that new issues aren’t being created. Implementing pipelines is only going to improve your timelines if it doesn’t create new issues that teams have to manage.
2. Poor communication across teams
As with all DevOps processes, good communication is key to successful continuous delivery processes. Teams need to be able to effectively work together to identify and resolve issues and work toward the same goal. Without good communication, accountability is lost and members may interfere with each other’s productivity.
When it comes to delivery processes, communication requirements also extend outside DevOps teams and pipelines. If management or customers are not communicating their needs or expectations to teams, delivery will be delayed. It is the responsibility of project managers to ensure that communication is clear and that all sides reach the same understanding.
3. Careless automation
Automation in pipelines can be a double-edged sword. Especially at first, it can be tempting to try to automate as many processes as possible, as quickly as possible. However, this is likely to lead to errors and may slow down your development. When automation is implemented, a misconfiguration can quickly scale out of control.
When processes are automated, teams may not notice issues before damage has been caused—or at all. This is particularly true if teams are not periodically auditing automations or if poorly designed processes were automated without optimization.
To avoid this, teams need to carefully and methodically evaluate processes and prioritize which automations will add the greatest value. These can then be created and implemented before more are added. This enables teams to better verify automation improvements and to correct an issue before it compounds.
5 Tips for Advancing Your DevOps Pipeline
When refining your DevOps pipeline, there are many different tools you can incorporate to improve performance and efficiency. But besides these tools, you can also implement a variety of best practices to optimize your pipeline.
1. Prioritize team management
Pipelines are meant to help DevOps teams; these tools are not replacements. Make sure that your pipeline supports and enhances team practices and workflows. Your tooling and configurations should not impede workflows or require teams to completely change processes.
Instead, pipelines should increase team visibility into software development lifecycle (SDLC) processes. DevOps pipelines should aid collaboration and ensure that relevant team members are aware of issues as soon as possible. Your pipeline should also enable your team to reliably detect and identify issues and respond accordingly.
2. Use your pipeline for all deployments
When you need immediate changes or patches, you can apply them ad hoc. Ad hoc patches are faster since you skip the continuous integration and continuous delivery (CI/CD) process. However, these patches are not integrated with your codebase. As a result, you may overwrite them with your next deployment. This kind of overwriting brings back the original issue and duplicates work.
Make sure to apply changes inside your pipeline. DevOps pipelines provide automated testing, benefits of standardization, and simple merge processes. Using your pipeline also ensures uniform software quality and increases efficiency in the long run.
3. Adopt microservices
Building your pipeline around microservices can build in flexibility, scalability, and availability that are not otherwise possible. Microservices can also enable you to more easily modify tooling, upgrade components, and adjust workflows. These services can also let you isolate vulnerabilities in your pipeline, improving system and data security.
If you are rebuilding your pipeline or building a new one from scratch, it should be relatively easy to implement microservices. However, adapting an existing pipeline takes more work. The easiest way to accomplish the latter is to replace services and tools piecemeal. This helps ensure that tools remain compatible and decreases any downtime you might experience.
4. Decouple applications
Coupling refers to the level of interdependency between different components in an application. Continuous integration builds and feedback cycles in systems that have low coupling are faster. Applications that have high coupling are difficult to maintain and test because changes to one component would affect the other components as well.
Refactoring such applications is also a complicated task. Make sure to decouple the components since high coupling can affect the success of a DevOps process. Decoupling can also help you create a robust test strategy with shorter test cycles.
5. Pause between deployments and releases
Some DevOps pipelines separate delivery and deployment procedures, such as by including manual steps between these two processes. These manual steps can involve additional testing or customer approval, or they can be used to space out releases.
Pausing before release can also enable you to add steps that are difficult or impossible to include in an automated process, such as using blue-green deployments to roll out upgrades and gradually shift users to the new version, or using A/B test deployments, which divide users into two groups to test features or versions of a product.
Prioritize Processes as well as Tools
When implementing the DevOps methodology, you need to assess the tools you have and decide which ones can support the delivery process. But even more important than tools, make sure to also take into account proven practices that can help you resolve issues related to integration and deployment. Remember to provide team members with greater visibility into the processes, maintain a Git repository of commonly used scripts, and add some manual steps to perform additional tests.
I am glad to see an article on this, as Agile displaced data from the development ecosystem. Instead of figuring out how to manage data in an agile manner, the community largely discarded practices pertaining to data.
This article provides some good guidance. The article is focused mostly on the topic of "big data", which is one aspect of data. I would suggest that for big data, one might also consider the importance of data modeling - something that many Agile teams (but not all) overlook. But do it in an agile manner - embed someone in the team who understands the organization's information model.
And there is the consideration of test data. The common Agile practice is to treat it entirely ad-hoc, but realistic test datasets are essential if you are to trust your tests. If devs cook up the test data, then of course the tests will pass - until the system goes live and starts to use real world production data.
And define meta data for your noSQL databases and your message schemas. Otherwise you will create a data cesspool instead of a data lake. Make the update of those metadata or schemas part of the definition of done.
Agile 2 talks about all these things.
Assuming that you are learning Devops to build a career around it, one of the most important things to know first is what is DevOps, why is it important and what problems can it solve in the Technology or business domain. You can easily get to know more about that by reading through various DevOps related articles, case studies, whitepapers, and even online videos. Additionally, you can familiarize yourself with the core concepts or a basic understanding of Agile, Cloud computing, Linux, etc. Once you accomplish all of that, you can prepare yourself better to choose the right and most effective method to get started with DevOps. There are many institutes offering online training programs on DevOps. You can do your own research and choose one that provides course content with a balanced mix of theoretical and practical knowledge of the concepts of DevOps, its tools, and practices. Wiculty is one such option that I am personally aware of wherein the course modules consist of deep-dive concept explanations and Live terminal execution by expert trainers carrying years of industry experience. The below link could also help you. https://www.stackoverdrive.net/