Kubernetes married with a cloud provider elastic, highly available infrastructure. Many CI engines today (Jenkins, Bamboo, Gitlab, CircleCI), provide native integration with kubernetes so that your build and deploy workload can be elastically executed. This allows your pipeline to meet the needs of your schedule, be it the 4pm pile on to commit code before going home, the mad rush to get a hot fix to production, or the surge of an unexpected customer ask. Gone are the days of the build queue growing and you CI engine collapsing under the weight of a hundred build requests. In order for a pipeline to leverage this capacity changes must be made to the pipeline architecture. Tools must be dockerized, the ephemeral nature of running docker must be considered, kubernetes specifications or helm charts must be generated for the application, automated testing must be adapted to work in the new architecture, and then there is the database. Each one of these issues, plus many others I’ve missed contained unfortunate, unforeseen pitfalls that translated in schedule delays. Join Glenn as he helps you short circuit the pitfalls of migrating to kubernetes off of your static in-elastic virtual infrastructure.

Kubernetes is quickly gaining popularity, but does the technology deserve so many accolades. In this talk we learn what exactly kubernetes has to offer in the DevOps world. We see that as a service it can provide resources for your DevOps pipeline, automated tests, and the application you are trying to build. We visit an example implementation of such a pipeline and discuss some lessons learned about how to balance these three needs. Additionally, we look at what design considerations your application needs to make, you should consider for your automated testing infrastructure, and how all of this can be leveraged to keep consistent CM while deploying rapidly. Lastly, we explore how threading it all together can help you fail fast and recover quickly as well.

Wonder how you can make your testing more efficient? Join Glenn Buckholz as he explores Docker, a technology that allows rapid development and deployment via containers. First, he explains exactly what composes a container, and discusses the differences between a container and an image. Once this is clear, Glenn demonstrates how Docker solves the problem of what he calls the state capture problem. When a test case produces a failure, the developer and testers often expend significant effort reproducing the issue so the developer can see the issue and fix it. Glenn demonstrates how Docker enables succinct, accurate, and quick communication between testers and developers, helping mitigate the state capture problem. In addition, testers can use Docker to load data, efficiently insert testing tools into a running system, set system state, and aid in test reproducibility. After you look at the inner workings of Docker and run through a few practical examples, you’ll find that Docker will hold an important place in your testing toolbox.

This presentation will demonstrate a case study at USCIS where a traditional waterfall model software development program was converted to Agile with the help of DevOps. The presentation will start by defining the initial state of the program. The techniques used to create an initial assessment will be discussed. Next comes an evaluations of objectives we prioritized after our initial assessment and why. Last, I'll thread together how our DevOps successes pushed better Agile adoption.