DevOps is all about how businesses must be results-oriented to meet evolving customer demand and gain greater market share. Today’s marketplace is connected, always on and increasingly competitive. Companies are adopting what we call an “as-a-service” approach to achieve better outcomes fast, consuming and leveraging leading-edge technologies such as cloud and automation.
As IT systems grow exponentially, and cloud solutions proliferate, non-automated, manual systems increasingly are becoming a major business liability. Today’s systems are simply becoming too big and complex to run completely manually, and working without automation is largely unsustainable for many enterprises across all industries.
Automation involves a set of tools, processes and insights that allows IT environments to self-modify and adjust, and some enterprises have started using intelligent automation to drive a new, more productive relationship between people and machines.
For example, IT automation is often used to auto-scale and load-balance large fleets of servers, manage global content distribution based on geographic demand, enable self-healing of IT systems and manage security mostly with limited ongoing manual intervention.
Moreover, automation enables the ability to adapt and improve the service experience without manual intervention. However, while these tools offer new strengths and capabilities, they are meant to complement and enhance human skills.
Effective automation depends on adequate insights collected from all the systems relevant to the service experience and business outcome you’re trying to augment. Insights from data is necessary to create opens paths to automated predictions and ultimately using machine learning, or artificial intelligence, as part of a full scope the as-a-service construct.
Specific insights known as telemetry allows signals to be harvested and interpreted so automation can better adjust production systems to maintain a healthy business. The insight gathered from such analytics allows automation to validate and compose modification rules. For example, sensors that detect a supply chain issue could automatically reroute or fine-tune related functions, such as dispatch or logistics, to solve or generate a workaround for the issue. The business flow can adapt and realign automatically with the ultimate goal of improving the customer experience.
Automation Creates High Resiliency
Two common business outcomes that depend on efficient automation are highly resilient systems and experimentation platforms.
Highly resilient systems include automation that can detect, avoid, heal and remediate any deviations from normal, healthy business function. To detect deviations, automation capabilities need to understand what the “steady state” of the system is and what constitutes the “health” of the system under varying conditions. For each detected deviation from an established steady state, a specific automation is triggered that attempts to return the system back to the steady state.
The best way to determine if resiliency automation works effectively is through a process known as “fault injection.” Highly resilient systems run under constant fire drills in which operations insert faults into the system while developers continuously build responding resiliency automation.
Automation Creates Higher Degree of Experimentation
Automation also can provide a higher degree of experimentation and increase agility, two key attributes of as-a-service economy. Automatically provisioning a component such as a virtual machine, for example, is only a piece of the puzzle since automation is most valuable when it contributes to improving a customer experience or delivering a business outcome.
A platform that’s constantly testing, experimenting and developing allows companies to try new ideas in production quickly without fear of failure or outage. When confidence in system resiliency is high, it allows businesses to test new things directly in production (A/B testing). If an experiment fails, there is no harm done as automation returns the system to steady state. If an experiment succeeds, it is quickly absorbed into the production itself.
A fast, efficient experimentation platform enables businesses to react faster to failures and successes—and pivot accordingly without excess wasted resources. For example, a retail company might change a shopping basket feature for 1 percent of its customers. With constant measurement and instrumentation, the company can automatically derive insights, determine if the change is effective and create a chain of automated reactions. If, say, the demand spikes for a new offering based on a limited customer pilot, the system can reset stocking levels ahead of geographic or further customer segment rollout. This ability increases a company’s agility and adaptability, improving the customer experience and delivering on the most important factors determining success in today’s as-a-service business environment.
Use Of Tools to Facilitate Devops
Tools are inherent to our jobs, inherent to how we solve the problems we face each day. Our comfort level with the set of tools that are available to us, and our ability to adapt to new tools as they evolve and shape our thoughts and ideas. The availability of collective knowledge within the palm of your hand combined with the collaboration across organization and company boundaries through open source software is dramatically disrupting the status quo of work. Companies mired in managing infrastructure configuration management by hand with unknown numbers of divergent systems, unable to quickly change and respond to market demands will struggle against their counterparts who have managed to contain their complexity on one axis through infrastructure automation. While it is possible to manage servers by hand, or even artisinally crafted shell scripts, a proper configuration management tool is
invaluable especially as your environment and team changes.
Even the best software developers will struggle if they are working in an environment without a version control system in place. Tools matter in that not having them, or using them incorrectly, can destroy the effectiveness of even the most intelligent and empathetic of engineers. The consideration you give to the tools you use in your organization will reflect in the overall organization’s success. You’ll find that what is a good tool for some teams might not be a good one for others. The strength of tools comes from how well they fit the needs of the the people or groups using them. If you don’t need feature X, its presence won’t be a selling point when considering which tool your organization should use. Especially in larger organizations with teams numbering in the dozens, finding one tool that meets the needs of every team will be increasingly difficult. You will have to strike a balance between deciding on one tool that will be used across the entire company consistently and allowing more freedom of choice among individual teams. There are benefits to both the consistency and manageability that comes from having only one tool in use in an organization, and also from allowing teams to pick specific tools that work best for then.
Because DevOps is a cultural shift and collaboration (between development, operations and testing), there is no single “DevOps tool”: it is rather a set (or “DevOps toolchain”), consisting of multiple tools in the Delivery and Deployment pipelines. Generally, DevOps tools fit into one or more of these categories, which is reflective of the software development and delivery process:
- Plan – Plan testing strategy, CI/CI Strategy, Choice of Tools etc
- Code — Code development and review, version controltools, code merging;
- Build — Continuous integrationtools, build status;
- Test — Test and results determine performance;
- Release — Change management, release approvals, release automation;
- Deploy — Infrastructure configuration and management, Infrastructure–as–Code tools;
- Operate and Monitor — Applications performance monitoring, end–user experience.
Though there are many tools available, certain categories of them are essential in the DevOps toolchain setup for use in an organization.
Tools such as Docker (containerization), Jenkins (continuous integration), Puppet (Infrastructure-as-Code) and Vagrant (virtualization platform)—among many others—are often used and frequently referenced in DevOps tooling discussions.
Typical stages in a DevOps toolchain looks like this