In late 2014, the SEI blog introduced a biweekly series of blog posts offering guidelines, practical advice, and tutorials for organizations seeking to adopt DevOps. These posts are aimed at the ever-increasing number of organizations adopting DevOps (up 26 percent since 2011). According to recent research, those organizations ship code 30 times faster. Despite the obvious benefits of DevOps, many organizations hesitate to embrace DevOps, which requires a shifting mindset and cultural and technical requirements that prove challenging in siloed organizations. Given these barriers, posts by CERT researchers have focused on case studies of successful DevOps implementations at Amazon and Netflix, as well as tutorials on popular DevOps technologies such as Fabric, Ansible, and Docker. This post presents the 10 most popular DevOps posts (based on number of visits) over the last six months.
Container-based virtualization platforms provide a means to run multiple applications in separate instances. Container technologies can provide significant benefits to DevOps, including increased scalability, resource efficiency, and resiliency. Unless containers are decoupled from the host system, however, there will be the potential for security problems. Until that decoupling happens, this blog posting describes why administrators should keep a close eye on the privilege levels given to applications running within the containers and to users accessing the host system.
At a recent workshop we hosted, a participant asked why the release frequency was so high in a DevOps environment. When working with significant legacy applications, release may be a once-in-a-year type event, and the prospect of releasing more frequently sends the engineering teams running for the hills. More frequent releases are made possible by properly implementing risk mitigation processes, including automated testing and deployment. With these processes in place, all stakeholders can be confident that frequent releases will be successful.
This post is the latest installment in a series aimed at helping organizations adopt DevOps. Some say that DevOps is a method; others say it is a movement, a philosophy, or even a strategy. There are many ways to define DevOps, but everybody agrees on its basic goal: to bring together development and operations to reduce risk, liability, and time-to-market, while increasing operational awareness. Long before DevOps was a word, though, its growth could be tracked in the automation tooling, culture shifts, and iterative development models (such as Agile) that have been emerging since the early 1970s.
The federal government continues to search for better ways to leverage the latest technology trends and increase efficiency of developing and acquiring new products or obtaining services under constrained budgets. DevOps is gaining more traction in many federal organizations, such as U.S. Citizenship and Immigration Services (USCIS), the Environmental Protection Agency (EPA), and the General Services Administration (GSA). These and other government agencies face challenges, however, when implementing DevOps with Agile methods and employing DevOps practices in every phase of the project lifecycle, including acquisition, development, testing, and deployment. A common mistake when implementing DevOps is trying to buy a finished product or an automated toolset, rather than considering its methods and the critical elements required for successful adoption within the organization. As described in previous posts on this blog, DevOps is an extension of Agile methods that requires all the knowledge and skills necessary to take a project from inception through sustainment and also contain project stakeholders within a dedicated team.
DevOps can be succinctly defined as a mindset of molding your process and organizational structures to promote
software quality attributes most important to your organization
As I have discussed in previous posts on DevOps at Amazon and software quality in DevOps, while DevOps is often approached through practices such as Agile development, automation, and continuous delivery, the spirit of DevOps can be applied in many ways. In this blog post, I am going to look at another seminal case study of DevOps thinking applied in a somewhat out-of-the-box way: Netflix.
This post is the latest installment in a series aimed at helping organizations adopt DevOps.
Tools used in DevOps environments such as continuous integration and continuous deployment speed up the process of pushing code to production. Often this means continuous deployment cycles that could result in multiple deployments per day. Traditional security testing, which often requires manually running multiple tests in different tools, does not keep pace with this rapid schedule. This blog post introduces a tool called Gauntlt, which attempts to remedy this issue.
When Agile software development models were first envisioned, a core tenet was to iterate more quickly on software changes and determine the correct path via exploration--essentially, striving to "fail fast" and iterate to correctness as a fundamental project goal. The reason for this process was a belief that developers lacked the necessary information to correctly define long-term project requirements at the onset of a project, due to an inadequate understanding of the customer and an inability to anticipate a customer's evolving needs. Recent research supports this reasoning by continuing to highlight disconnects between planning, design, and implementation in the software development lifecycle. This blog post highlights continuous integration to avoid disconnects and mitigate risk in software development projects.
Have you ever been developing or acquiring a system and said to yourself, I can't be the first architect to design this type of system. How can I tap into the architecture knowledge that already exists in this domain? If so, you might be looking for a reference architecture. A reference architecture describes a family of similar systems and standardizes nomenclature, defines key solution elements and relationships among them, collects relevant solution patterns, and provides a framework to classify and compare. This blog posting, which is excerpted from the paper, A Reference Architecture for Big Data Systems in the National Security Domain, describes our work developing and applying a reference architecture for big data systems.