2022 Gartner® Magic Quadrant™ SIEM
Get the reportMore
At its core, DevOps is a set of principles. There are certain types of tools and processes that reinforce those principles and put them into practice. But the tools and processes are just a means to an end. The end itself is DevOps principles.
Thus, DevOps success boils down to understanding the key DevOps principles, then identifying the tools and processes that enable your team to achieve them. The tools and processes used to achieve DevOps vary from organization to organization, but DevOps principles themselves are universal.
Following are the seven essential principles of DevOps.
When the DevOps concept originated in the late 2000s, the driving idea behind it was that developers and IT operations teams should work together more efficiently. DevOps was seen as the solution to the challenge of having developers write code without understanding the needs of the IT engineers who had to deploy and manage it, and vice versa.
Today, many definitions of DevOps extend this idea to include other types of IT professionals, such as software testers and security engineers, in addition to developers and IT operations.
Yet the core principle of collaboration has remained unchanged. No matter what your team looks like, DevOps success hinges in part on your ability to enable all members of your team to work together effectively toward a common goal. In this way, you avoid a team of different groups pursuing their own agendas without insight into what other teams are doing. This leads to conflicts and counterproductivity.
In addition to working together, different individuals and groups within a DevOps team must be able to communicate. Communication may seem to fall under the umbrella of collaboration, but it’s important and complicated enough to be a DevOps principle in its own right.
After all, developers, IT engineers, software testers and other roles within IT organizations all have different types of expertise, and speak different languages. For example, an IT engineer who knows how to deploy and monitor an application may not be familiar with the lingo of coding that developers use. And even if teams can speak to each other effectively, it can be difficult for one group to know how to open a conversation with others in the first place.
Therefore, promoting seamless communication between all stakeholders in the DevOps team is a core principle of DevOps. Communication is facilitated by, among other practices, having central communication platforms that all team members use, and aggregating relevant data into a central location where everyone can access it.
Observability refers to the ability of the DevOps team to track, monitor and measure the state of their pipeline and operations. Without observability, DevOps engineers end up shooting in the dark, unaware of what is working and what is not.
Observability is achieved through a variety of tools and practices. Communication is one facet of observability. It is only by communicating that one part of the DevOps team knows what others are doing. Log collection, aggregation and analysis are critical too, because they reveal deep insight into software availability, reliability and performance based on data from multiple sources.
Another key DevOps principle is the concept of constant improvement. Doing DevOps successfully means always finding ways to make the software delivery process and the application itself better in terms of reliability, performance, security and any other factors that improve the experience of the DevOps team or the end-users it supports.
An important caveat is that striving for constant improvement is not the same thing as being a perfectionist. An effective DevOps team doesn’t wait for code to be perfect before releasing it. That strategy harkens back to the days of “waterfall” delivery, when application changes took months or years to arrive.
Instead, a healthy approach to DevOps is to release new code or features early and often, monitor them to gain insight into what works well and what doesn’t, and then use subsequent release cycles to improve.
Shift-left is the practice of performing processes as early in the DevOps pipeline as possible. In other words, you shift them “to the left” of the pipeline.
The goal of shift-left is to start processes sooner so that problems arising from them can be identified and addressed more quickly – ideally, before software is released into production.
For example, software testing can be shifted left by beginning automated tests as soon as code is integrated. This practice diverges from the conventional approach to testing, which was to wait until code was built and just about to be deployed before testing it. The latter approach is problematic because if tests reveal a problem, correcting it is more difficult, because it requires rolling back a release that was about to be deployed.
By extension, more delays arise. With a shift-left approach, however, testing can be done early and often, simplifying the process of addressing issues.
Declarative management refers to the practice of writing configuration files that define how a process should operate or a software environment should look, then using automation tools (such as an Infrastructure-as-Code platform) to enforce those configurations.
Declarative configuration management is the opposite of imperative configuration, wherein engineers manually configure applications or environments to match a desired state.
The advantages of declarative configuration management include consistency and scalability. Once written, a configuration can be applied and reapplied as many times as needed, across as large an environment as desired.
Last but not least among the principles of DevOps is automation.
You could argue that it’s theoretically possible to achieve the other DevOps principles without automation. But you’d be hard-pressed to build a DevOps pipeline in practice that works without automation.
For DevOps teams, automation is a central principle that reinforces all of the other principles of DevOps (which is why it’s the final principle on this list). It promotes better collaboration and communication by allowing engineers to automate redundant tasks, so that they can spend more time working with each other and less time doing tedious manual work. It also facilitates observability, improvement and shift-left practices by ensuring that processes are smooth and consistent. And it goes hand-in-hand with declarative configuration management.
In practice, it’s usually not possible to automate every aspect of a DevOps pipeline. But teams should strive to automate whenever and wherever possible.
Sumo Logic helps put DevOps principles into practice in several ways.
Most obvious, Sumo Logic helps collect and analyze log data in order to improve observability. But its DevOps-centric functionality extends far beyond this.
Sumo Logic also enables DevOps by streamlining and automating log management and analytics across disparate systems. No matter which applications or operating systems create your log data, where it is stored or which teams manage it, Sumo Logic makes it easy to aggregate the data into a central location. It also lets you analyze and visualize the data in a way that makes it actionable for all stakeholders. And because configurations for Sumo Logic collectors are written as code, they can be reused consistently as your organization scales
These are all reasons to include Sumo Logic in your DevOps toolset. See for yourself with a free, no credit-card required trial.
Reduce downtime and move from reactive to proactive monitoring.
Build, run, and secure modern applications and cloud infrastructures.Start free trial
Observability has become one of the most important areas of your application and infrastructure landscape, and the market has an abundance of tools available that seem to do what you need. In reality, however, most products – especially leading open-source based products – were created to solve a single problem extremely well, and have added additional supporting functionality to become a more robust solution; but the non-core functionality is rarely best of breed. Examples of these are Prometheus and Grafana.