Book Image

DevOps Culture and Practice with OpenShift

By : Tim Beattie, Mike Hepburn, Noel O'Connor, Donal Spring, Ilaria Doria
Book Image

DevOps Culture and Practice with OpenShift

By: Tim Beattie, Mike Hepburn, Noel O'Connor, Donal Spring, Ilaria Doria

Overview of this book

DevOps Culture and Practice with OpenShift features many different real-world practices - some people-related, some process-related, some technology-related - to facilitate successful DevOps, and in turn OpenShift, adoption within your organization. It introduces many DevOps concepts and tools to connect culture and practice through a continuous loop of discovery, pivots, and delivery underpinned by a foundation of collaboration and software engineering. Containers and container-centric application lifecycle management are now an industry standard, and OpenShift has a leading position in a flourishing market of enterprise Kubernetes-based product offerings. DevOps Culture and Practice with OpenShift provides a roadmap for building empowered product teams within your organization. This guide brings together lean, agile, design thinking, DevOps, culture, facilitation, and hands-on technical enablement all in one book. Through a combination of real-world stories, a practical case study, facilitation guides, and technical implementation details, DevOps Culture and Practice with OpenShift provides tools and techniques to build a DevOps culture within your organization on Red Hat's OpenShift Container Platform.
Table of Contents (30 chapters)
Free Chapter
2
Section 1: Practices Make Perfect
6
Section 2: Establishing the Foundation
11
Section 3: Discover It
15
Section 4: Prioritize It
17
Section 5: Deliver It
20
Section 6: Build It, Run It, Own It
24
Section 7: Improve It, Sustain It
27
Index
Appendix B – Additional Learning Resources

Design of Experiments

All our ideas for new products, services, features, and indeed any changes we can introduce to make things better (more growth, increased revenue, enhanced experience, and so on) start off as a hypothesis or an assumption. In a traditional approach to planning, a team may place bets on which experiment to run based on some form of return on investment-style analysis, while making further assumptions in the process.

Design of Experiments is an alternative to this approach, in which we try to validate as many of the important ideas/hypotheses/assumptions we are making as early as possible. Some of those objects of the experiments we may want to keep open until we get some real-world proof, which can be done through some of the advanced deployment capability (such as A/B Testing) that we'll explore later in this chapter.

Design of Experiments is a practice we use to turn ideas, hypotheses, or assumptions into concrete, well-defined sets of experiments...