Ever since the concept of DevOps was introduced around 2009, a flood of new DevOps tools and uses have been adopted by engineering teams.
The term DevOps describes a model of agile development and operations, which encompasses both practices and tools that teams use to build software faster. Prior to the introduction of the DevOps operating model, software development followed a “waterfall” model, where, in essence, developers would code first, then conduct quality assurance (QA) testing, and loop back as needed to address bugs or problems.
The DevOps model is much more integrated; it consists of breaking the development process down into much smaller increments.
Instead of building the entire application before testing, under the DevOps model, developers code and test daily. Day to day collaboration is the norm, and the entire model works on a continuous process of improvement.
Practitioners of this agile framework need a number of DevOps automation tools for deployment, continuous integration and monitoring, security, and cost management.
Below are some of the most popular DevOps tools and their uses.
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Red Hat defines configuration management as “... a process for maintaining computer systems, servers, and software in a desired, consistent state.” DevOps tools for configuration management automate the process.
In a world where you’re managing fleets of compute resources — in most cases, EC2 compute resources, where you need to provision, reconfigure, maintain, and hatch services and servers — you’re probably going to be using one of the above configuration management solutions.
G2 crowd defines configuration management as, “the process of tracking and conducting changes made to applications during the development process. Configuration management software tracks changes to applications and their infrastructure to ensure configurations are in a known and trusted state, and configuration details don’t rely on DevOps tribal knowledge.”
All of these tools can trace their lineage to the physical data center world. AWS Systems Manager is Amazon’s answer for this; it was developed specifically for the cloud.
If your company is cloud-native, you may have moved on to an approach where your code is your infrastructure, and instead of patching systems, you simply recreate them from scratch as needed. While some of these tools have evolved to try to support these types of changing use cases, the world is moving away from managing fleets of compute infrastructure.
Continuous integration (CI) tools automatically integrate code changes from multiple contributors into a single software application. Automated testing then validates all code changes.
Continuous deployment (CD) tools automatically deploy all validated code changes to customers; only a failed test prevents new changes from being deployed.
One of the best DevOps solutions is GitLab, in my opinion. It has been around on market longer than some of today's popular tools like GitHub CI or CircleCI.
So why GitLab is my choice? It's not a secret that GitLab has both version control system and CI tools - GitLab CI, so it's a perfect combo. But this combo is also available for GitHub, as well as you can create a GitHub or Bitbucket repo and integrate it with CircleCI.
If you'll take a closer look - you'll notice that GitHub and CircleCI are cloud-only services, so you can't download them and use them in your local network for learning purposes or install them in your corporate network as your DevOps tools. GitLab may be used in both ways - locally and in the cloud.
Local method may be useful for projects, where you have some services that will be required in your CI processes, for example:
1. Reports storage server (reportportal.io, Allure Server).
2. Browser management server for running end-to-end tests (Selenium Server, Selenium Grid, Selenoid).
Of course, you may call these services from your cloud CI, but it requires them to be available in a global network (internet). In this case, you'll have to think over the way to secure access to your services and the way to store credentials for them (project environment variables, for example).
So, as we can see, GitLab is a perfect solution for both local and cloud usage.
GitHub Actions, is a CICD tool similar to Travis or Jenkins. We love it because of how tightly integrated it is into the GitHub core experience. We mainly use it for testing and will likely use it in the future for automated publishing of the command line tool we build for our customers.
DevOps tools used for continuous integration have really taken hold because they address the key tenet of DevOps, which is that teams are constantly building and deploying software in an automated fashion. This can occur dozens or hundreds of times per day.
DevOps deployment tools make deployment an easy, reliable, repeatable process, something all engineering teams aspire to maintain. Even if the team is only deploying new code weekly, there’s no good reason to not be using a DevOps tool for continuous integration.
Continuous monitoring (CM) is the crucial final step in the DevOps pipeline. The rapid pace of deployment and constant change processes in the DevOps model requires tracking, identifying, and understanding key metrics. CM also aids in resolving infrastructure issues. Observability differs from monitoring in that it allows assessment of the state of internal systems by observing external outputs.
Monitoring is important to make sure bad things — ranging from an outage to a security breach — don’t happen and go undetected. You want to be able to see problems as quickly as possible so you can react.
Monitoring has largely been dominated by security information, event management solutions, or log aggregation solutions — but it is more frequently being supplanted by observability. Monitoring typically tracks what you’ve anticipated can go wrong; what you really want is a solution that will help you prepare for failures you didn’t see coming.
Observability is about instrumenting systems so that not only can you detect issues quickly, but after the fact, you have data necessary to understand what happened. In a broader sense, observability is about how well the internal state of systems can be inferred from the knowledge you gain by observing its outputs.
There are a lot of reasons to use an observability tool; the cloud is largely driven by emergent properties and activities you can’t predict. Things break and DevOps tools for continuous monitoring and observability help you pinpoint the source of the failure and get back on track more quickly.
Log management consists of the processes and policies governing generation, analysis, transmission, archiving, storage, and disposal of the log data created within an application. Automated log management tools do the work of handling these large volumes of data.
Logs are a fact of life. Applications generate an enormous amount of data, and you’re constantly referring to that data to understand how your systems are behaving.
There are also compliance requirements that demand that you record the activity of applications for compliance and security: who logged in and when, and what they did while they were logged in. This information needs to be stored, sometimes for years, depending on the requirements for your software. There really isn’t a reason any developer wouldn’t be using these tools.
In From Pots and Vats To Programs and Apps by Gordon Haff and William Henry of Red Hat, Haff defines Kubernetes as follows:
“Kubernetes, or k8s, is an open-source platform that automates Linux container operations. It eliminates many of the manual processes involved in deploying and scaling containerized applications. In other words, you can cluster together groups of hosts running Linux containers, and Kubernetes helps you easily and efficiently manage those clusters.”
Automated tools for container management do the work of orchestrating containerized resources efficiently.
My favorite DevOps tool is Kubernetes. As a professional in the cloud computing space, I appreciate that Kubernetes opens up multiple possibilities with multi-cloud and hybrid cloud implementations. It also facilitates the building of cloud-native applications with agility and speed.
The majority of the developers I have worked with on various projects agree that the container orchestration and management that Kubernetes offers
Arkade is an open-source DevOps tool that allows you to spin up various Kubernetes services, without having to remember dozens of configuration options or paths. It’s reducing the options to the bare minimum by setting sensible defaults. A real-time saver for day-to-day DevOps work.
Kubernetes have taken hold in the computing world and with them the complex problem of orchestration and management of containers. A number of solutions have been introduced to help you manage containerized compute resources.
In the fast-paced DevOps model, the question is not if incidents will occur, but rather when. Incident response and management involves detection, response, resolution, and analysis, to support readiness for the next incident. Incident response and management tools automate the process of detecting incidents and alerting the right people to the incident.
DevOps automation tools for incident response and management essentially focus on who’s on call — who gets the notification when something goes wrong? How you report and manage the incident process itself is important, and these tools help with that.
However, your organization may not be large enough to need one of these solutions; you may have decided to take a different path and already be building some of these capabilities into your observability and monitoring processes.
DigitalOcean defines infrastructure as code (IaC) as “the approach of automating infrastructure deployment and changes by defining the desired resource states and their mutual relationships in code.” Tools for implementing IaC automate the process of infrastructure reconfiguration.
HashiCorp provides a number of services for managing infrastructure, but is widely known for Terraform. Terraform and Confirmation are two ways to declaratively define your cloud infrastructure and are a key part of cloud DevOps.
You should not be defining infrastructure in the AWS Console; you should be defining it in code, checking it in, versioning it, and maintaining it. Any healthy DevOps cloud tooling or cloud strategy should follow an infrastructure as code pattern.
Gremlin defines chaos engineering as “... a disciplined approach to identifying failures before they become outages.” Chaos engineering tools run automated “what if?” experiments to help developers build resiliency into their systems.
DevOps tools in this category, like Gremlin, have a framework for managing chaos experiments and understanding the blast radius of failures within your organization.
These tools are based on the premise that everything fails eventually within a cloud computing environment; there are a lot of emergent properties that are difficult to test. Chaos engineering is the process of injecting failure into your systems and being able to observe the impact of that failure.
If you have a large, complicated system, you should be doing chaos engineering. Some of the largest cloud software companies in the world use it to help them predict where things can go wrong and, when they do, to help identify the problem.
In the collaborative DevOps model, security is a shared responsibility to be integrated from end to end. DevOps tools for security automate some security gates to keep the workflow from slowing down.
Security should be part of the development process, and there are a lot of tools for helping you manage it — Snyk for analyzing code as part of your continuous integration and development process, Threat Stack for analyzing your infrastructure, Fugue for securely deploying your infrastructure, and Secure Code Warrior to focus on your application security.
Cloud cost optimization is the process of efficiently managing cloud resources to ultimately reduce wasted spend. Cloud cost intelligence goes beyond cost optimization to connect cloud costs to the engineering activities that generate them. A number of tools have emerged to help developers manage cloud costs, including:
Each of these tools addresses either cloud cost management, continuous cloud optimization, point solutions for RI management, or Kubernetes/container cost management.
Some of the AWS tools are free but have limited value for managing large cloud budgets. Many of the others are legacy tools; they were developed for managing costs in a physical infrastructure computing environment and have evolved as computing has moved to the cloud.
Cloud cost intelligence goes beyond cloud cost management or optimization by correlating costs to the processes that generate them. A cloud cost intelligence tool allows engineers to understand the cost of the systems they are building — as they are building them. With this knowledge, cost can become a priority metric in the development process, along with security and other critical metrics.
The automated analysis and correlation of processes with associated costs allows you to be more proactive in managing costs, leading to more profitable applications.
CloudZero covers the spectrum of cloud compute costs, analyzing complex workloads — including Kubernetes cost breakdowns — to provide engineers with the data they need to build cost-efficient software.
Cost anomaly monitoring is continuous and not only automatically alerts the right individual or team to anomalies when they occur, but also identifies their source, so they can be addressed quickly to limit waste.
Unlike many other cloud cost optimization tools, CloudZero is cloud-native. It was designed from the start to monitor and analyze cloud costs — not to manage physical compute resources.
Most importantly, cloud cost intelligence allows your team to do more than just manage and optimize cloud costs. CloudZero’s cost-mapping allows you to easily see how your cloud spend aligns with your business strategy and objectives — and how to bring it into alignment with what matters most for your business.