The concept of DevOps has taken the tech world by storm. While some are drawn to the field for its attractive salaries, true success in DevOps requires more than just technical skills.
This article dives deeper than the hype. I've spent years implementing DevOps across various companies, drawing on my prior experience as a system administrator and IT director. Now, as CTO & Co-founder of Gart Solutions, I want to share my insights on what it truly means to be a DevOps professional.
Who embodies the DevOps spirit?
The question "what is DevOps?" sparked this article. There's a misconception that DevOps is a specific job title, a collection of tools, or a separate developer department.
DevOps is a cultural shift, a way of working. It's a set of practices that foster collaboration between developers and system administrators. DevOps doesn't change core roles, but it blurs departmental lines and encourages teamwork. Its goals are clear:
Frequent software updates
Faster software development
Streamlined and rapid deployment
There's no single "DevOps tool." While various tools come into play at different stages, they all serve a common purpose.
Having interviewed DevOps candidates for over 5 years, I've observed a crucial gap: those who view DevOps as a job title often face challenges collaborating with colleagues.
Here's a real-life example: a candidate with an impressive resume came in. His short stints at previous startups (5-6 months each) raised concerns. He blamed his departures on unsuccessful ventures and a company where "nobody understood him." Apparently, developers used Windows, and his role was to "package" their code in Docker for the CI/CD pipeline. He spoke negatively about his colleagues and workplace, suggesting a lack of problem-solving skills.
A key question I ask all candidates is:
"What does DevOps mean to YOU?"
I'm interested in their personal understanding. This particular candidate knew the textbook definition but disagreed, clinging to the misconception of DevOps as a job title. This misunderstanding is the root of his struggles, and it's likely true for others with the same perspective.
Employers, drawn to the "magic" of DevOps, seek a silver bullet – a single person to create this magic. Applicants who view DevOps as a job title don't grasp that this approach leads to disappointment. Some might have just added "DevOps" to their resume to ride the trend or for the higher salary, without truly understanding the philosophy.
DevOps Methodology: Putting Theory into Practice
DevOps methodology isn't theoretical; it's about practical application. As mentioned earlier, it's a toolbox of practices and strategies used to achieve specific goals. These practices can vary significantly depending on a company's unique business processes, but that doesn't make one approach inherently better or worse. It's all about finding the right fit.
DevOps Philosophy: Capturing the Essence
Defining DevOps philosophy can be tricky. There's no single, universally accepted answer, likely because practitioners are so focused on getting things done that formalizing the philosophy takes a back seat. However, it's a crucial aspect with deep ties to engineering practices.
Those who delve deeply into DevOps often develop a sense of "spirit" or a holistic understanding of how all company processes interconnect.
Drawing on my experience, I've attempted to formalize some core principles of this philosophy:
DevOps is Integrated: It's not a separate entity; it permeates all aspects of a company's operations.
Company-wide Adoption: For DevOps to be successful, all employees should consider its principles when planning their work.
Streamlining Efficiency: DevOps aims to minimize the time spent on internal processes, ultimately accelerating service development and enhancing customer experience.
Proactive Problem-Solving: In simpler terms, DevOps encourages a proactive approach from every employee to reduce time wasted and improve the quality of the IT products we use.
These "postulates," as I call them, are a springboard for further discussion. They offer a starting point to grasp the core tenets of the DevOps philosophy.
The Heart of DevOps: Communication
Communication is the lifeblood of DevOps. Because it's a cultural shift and a set of practices, strong communication skills are even more important than technical knowledge for a DevOps engineer.
There's also a fascination with specific tools like Kubernetes. However, using a tool like Kubernetes everywhere, regardless of suitability, can be counterproductive. It's crucial to choose the right tool for the job. While Kubernetes offers advantages, maintaining a cluster can be more expensive than a traditional deployment scheme.
DevOps implementation isn't cheap; it needs to justify its cost through overall business benefits. DevOps engineers act as pioneers, introducing new methodologies and building processes within companies. Success hinges on continuous communication with employees at all levels – from entry-level team members to leadership. Everyone needs to understand the "why" behind DevOps practices for successful implementation.
DevOps engineers might also need to navigate resistance to change. For example, developers accustomed to managing their entire project infrastructure locally on powerful laptops may resist shifting to a more streamlined approach. This is where communication becomes crucial in explaining the benefits of new methods.
However, many teams readily embrace new tools and actively participate in the DevOps transformation. Regardless of the initial reception, clear communication between the DevOps engineer and the team is essential for a smooth transition.
DevOps: Not a One-Size-Fits-All Solution
DevOps isn't a magic bullet for every company. It's crucial to understand when it makes sense and when it doesn't.
Generally, small businesses with static websites or information services that don't directly drive customer satisfaction likely won't see a significant benefit from DevOps.
Here's a good rule of thumb: If a company's success hinges on the quality, availability, and targeted features of its IT products used for customer interaction, then DevOps becomes a strong consideration.
Take a bank, for example. They rely heavily on IT infrastructure for customer interactions, making them a prime candidate for DevOps adoption.
Company Size Isn't the Only Factor
Don't assume that large companies automatically need DevOps. Consider an automaker whose primary customer interaction happens through dealerships via email and phone. Their internal operations may be well-managed with traditional system management approaches, negating the need for a full-blown DevOps implementation.
However, company size doesn't always dictate the need for DevOps. Many small software companies thrive with the DevOps methodology because it allows them to compete effectively.
The Key Factor: IT Product Importance
The ultimate question is: how critical are your IT products to your company's success and customer satisfaction?
If software is your primary revenue driver, regardless of other products or services, then DevOps is likely a worthwhile investment. Online stores and mobile game apps are perfect examples.
Our Expertise at Gart Solutions
At Gart Solutions, we understand the transformative power of DevOps for businesses of all sizes. Our team of passionate DevOps engineers fosters a culture of knowledge sharing through internal training and workshops.
Here's a glimpse into our DevOps toolbox:
CI/CD Pipeline Automation: We leverage industry-leading tools to streamline your software builds, testing, and deployments, ensuring speed and efficiency.
Infrastructure Management: Our experts handle your infrastructure setup, maintenance, and optimization, allowing you to focus on core development activities.
Containerization with Kubernetes: We provide expertise in deploying and managing containerized applications using Kubernetes, offering scalability and portability.
Cloud Solutions: Our team helps you navigate the world of cloud computing, from migration strategies to ongoing cloud management, optimizing your cloud footprint and costs.
Security & Monitoring: We prioritize robust security measures throughout your development lifecycle, while implementing comprehensive monitoring to ensure optimal application performance.
We constantly evaluate and integrate new tools and techniques to ensure our clients have access to the most cutting-edge solutions. Our goal is to be your trusted partner, empowering you to achieve your business objectives through the power of DevOps.
Watch our webinar about DevOps ROI & Value, by Fedir Kompaniiets, founder and CEO of Gart Solutions.
https://youtu.be/k7ao6akcxus?si=1nZPR7CIjM_a_ojO
In Conclusion: The Power of Communication and Collaboration
The journey to becoming a top-tier DevOps engineer hinges on a core skill: effective communication. DevOps is not a solo act; it's about fostering collaboration and breaking down silos. The most successful practitioners take the initiative to connect with colleagues, actively propose solutions, and embrace open discussions. Be prepared for ideas to evolve through team interaction – that's the magic of DevOps!
How can AI tools enhance DevOps efficiency?AI tools like ChatGPT, Claude, GitHub Copilot, and VZero are transforming DevOps by automating coding, streamlining infrastructure management, and accelerating UI prototyping. These tools reduce development time, minimize human error, and free up engineers for strategic tasks.
We’re long past the debate about whether AI will take over jobs. In DevOps, AI is already reshaping how we work—automating routine tasks, assisting in decision-making, and enhancing speed and productivity.
Just two years ago, using AI for code generation was off-limits in many companies. Today, it’s not only permitted — it’s encouraged. The shift has been fast and profound.
In this guide, I’ll share real-world use cases of how I use AI tools as a DevOps engineer and cloud architect, showing you where they fit into daily workflows and how they boost performance.
The Rise of AI Assistants in DevOps
Let's dive into something that’s been on everyone’s radar lately: AI assistants. But don’t worry, we’re not going to talk about AI taking over our jobs or debating its future in society. Instead, let’s get practical and look at how we’re already using AI assistants in our daily work routines.
Just two years ago, when ChatGPT 3.5 was launched, most people couldn’t have predicted just how quickly these tools would evolve. AI’s rapid progress has been especially game-changing for the IT field. It’s as if IT professionals decided, "Why not automate parts of our own jobs first?" And here we are, seeing the impact of that decision. In just two years, AI has made strides that feel almost unreal.
I remember when many companies had strict no-AI policies. Legal restrictions were everywhere—using AI to analyze or write code was off the table. Fast forward to now, and it’s a whole different story. Many companies not only allow AI; they actively encourage it, seeing it as a way to work faster and more effectively. Tasks that used to take days can now be handed off to AI, letting us focus on deeper engineering work.
Today, I want to take you through how I, as a DevOps engineer and cloud architect, am using AI assistants to streamline different parts of my job.
https://youtu.be/4FNyMRmHdTM?si=F2yOv89QU9gQ7Hif
Key AI Tools in DevOps and Their Use Cases
ChatGPT: Your All-in-One Assistant for DevOps
Let’s start with ChatGPT. By now, it’s a household name, probably the most recognized AI assistant and where so much of this tech revolution began. So, why do I rely on ChatGPT?
First off, it’s built on some of the largest AI models out there, often debuting groundbreaking updates. While it might feel more like a generalist than a specialist in niche areas, its capabilities for everyday tasks are impressive.
I won’t go into too much detail about ChatGPT itself, but let’s look at some recent updates that are genuinely game-changing.
For starters, ChatGPT 4.0 is now the new standard, replacing previous models 3.5 and 4. It’s a foundational model designed to handle just about any task, as they say.
But the real excitement comes with ChatGPT’s new Search feature. This is a huge leap forward, as the model can now browse the internet in real-time. Previously, it was limited to its last training cutoff, with only occasional updates. Now, it can look up current information directly from the web.
Here’s a quick example: You could ask, “What’s the current exchange rate for the Ukrainian hryvnia to the euro?” and ChatGPT will fetch the latest answer from the internet. It can even calculate taxes based on the most recent rates and regulations.
Even better, you can see the sources it uses, so you can double-check the information. This feature positions ChatGPT as a potential Google alternative for many professional questions.
Another exciting addition is ChatGPT Canvas, which offers a more visual and interactive way to collaborate with the AI. This feature lets you create and adjust diagrams, flowcharts, and other visuals directly in the chat interface. It’s perfect for brainstorming sessions, project planning, and breaking down complex ideas in a more visual format.
Personally, I use ChatGPT for a range of tasks — from quick questions to brainstorming sessions. With Search and Canvas, it’s evolving into an even more versatile tool that fits a variety of professional needs. It’s like having an all-in-one assistant.
To summarise, ChatGPT is good for:
🔍 Real-Time Web Access with Search
ChatGPT’s built-in browser now retrieves up-to-date information, making it more than a static assistant. Whether you're checking the latest AWS pricing or debugging region-specific issues, this tool has you covered.
🧠 Complex Task Handling
From brainstorming pipeline structures to writing Bash scripts, ChatGPT handles high-level logic, templating, and document writing.
🗂️ Canvas: Visualizing Ideas
With Canvas, you can sketch infrastructure diagrams, brainstorm architectures, or visually debug pipeline issues—all within the same AI environment.
Use it for:
YAML templating
Cost estimation
Visual breakdowns of infrastructure
Researching live documentation
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Claude: AI for Project Context and Helm Charts
Claude’s project memory and file management capabilities make it ideal for large, structured DevOps tasks.
Let’s dive into a more specialized AI tool I use: Claude. Unlike other AI assistants, Claude is structured to manage files and data in a way that’s incredibly practical for DevOps. One of the best features? The ability to organize information into project-specific repositories. This setup is a huge help when juggling different environments and configurations, making it easier to pick up complex projects exactly where you left off.
Here’s a quick example. Imagine I need to create a new Helm chart for an app that’s been running on other machines.
My goal is to create a universal deployment in Kubernetes. With Claude, I can start a project called "Helm Chart Creation" and load it up with essential context—best practices, reference files, and so on. Claude’s “Project Knowledge” feature is a game-changer here, allowing me to add files and snippets it should remember. If I need references from Bitnami’s Helm charts, which have an extensive library, I can just feed them directly into Claude.
Now, say I want to convert a Docker Compose file into a Helm chart. I can input the Docker Compose file and relevant Helm chart references, and Claude will scaffold the YAML files for me. Sure, it sometimes needs a bit of tweaking, but the initial output is structured, logical, and saves a massive amount of time.
In a recent project, we had to create Helm charts for a large number of services. A task that would’ve previously taken a team of two to four people several months now took just one person a few weeks, thanks to Claude’s ability to handle most of the code organization and structuring.
The only downside? You can only upload up to five files per request. But even with that limitation, Claude is a powerful tool that genuinely understands project context and writes better code.
To summarise, Claude is good for:
🧾 Project Knowledge Management
Organize your tasks by repository or project. Claude remembers past inputs and references, making it useful for tasks like:
Converting Docker Compose to Helm
Creating reusable Helm charts
Structuring Kubernetes deployments
GitHub Copilot for Code Generation
Next up, let’s talk about Copilot for Visual Studio. I’ve been using it since the early days when it was just GitHub Copilot, and it’s come a long way since then. The latest version introduces some great new features that make coding even more efficient.
One small change is that Copilot now opens on the right side of the Visual Studio window—just a layout tweak, but it keeps everything organized. More importantly, it now taps into both OpenAI models and Microsoft’s proprietary AI, plus it integrates with Azure. This means it can work directly within your cloud environment, which is super useful.
Copilot also gets smart about your project setup, reading the structure and indexing files so it understands what you’re working on. For example, if I need to spin up a Terraform project for Azure with a Terraform Cloud backend, I can just ask Copilot, and it’ll generate the necessary code and config files.
It’s great for speeding up code writing, starting new projects, and even handling cloud services, all while helping troubleshoot errors as you go. One of my favorite features is the “Explain” option. If I’m stuck on a piece of code, I can ask Copilot to break it down for me, which saves me from searching online or guessing. It’s a real timesaver, especially when working with unfamiliar languages or code snippets.
GitHub Copilot is good for:
🚀 Cloud-Specific Code Generation
Copilot now understands infrastructure-as-code contexts:
Launch a Terraform project for Azure in minutes
Create config files and debug errors automatically
💬 Code Explainability
One standout feature is the “Explain this code” function. If you're unfamiliar with a script, Copilot explains it clearly—perfect for onboarding or refactoring.
Use it for:
Cloud provisioning
Writing CI/CD scripts
Boilerplate code in unfamiliar languages
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VZero for UI and Front-End Prototyping
Finally, let’s take a look at VZero from Vercel. I don’t use it as often as other tools, but it’s impressive enough that it definitely deserves a mention.
VZero is an AI-powered tool that makes creating UI forms and interfaces fast and easy. For someone like me—who isn’t a frontend developer—it’s perfect for quickly putting together a UI concept. Whether I need to show a UI idea to a dev team, share a concept with contractors, or visualize something for stakeholders, VZero makes it simple.
For example, if I need a page to display infrastructure audit results, I can start by giving VZero a basic prompt, like “I want a page that shows infrastructure audit results.” Even with this minimal direction, VZero can create a functional, attractive UI.
One of the best things about VZero is how well it handles design context. I can upload screenshots or examples from our existing website, and it’ll match the design language—think color schemes, styles, and layout. This means the UI it generates not only works but also looks consistent with our brand.
The tool even generates real-time editable code, so if I need to make a quick tweak—like removing an extra menu or adjusting the layout—it’s easy to do. I can just ask VZero to make the change, and it updates the UI instantly.
There are two main ways I use VZero:
Prototyping: When I have a rough idea and want a quick prototype, VZero lets me visualize it without having to dive into frontend code. Then, I can pass it along to frontend developers to build out further.
Creating Simple Forms: Sometimes, I need a quick form for a specific task, like automating a workflow or gathering input for a DevOps process. VZero lets me create these forms without needing deep frontend expertise.
Since VZero is built on Vercel’s platform, the generated code is optimized for modern frameworks like React and Next.js, making it easy to integrate with existing projects. By using AI, VZero cuts down the time and effort needed to go from idea to working UI, making frontend design more accessible to non-experts.
VZero is good for:
✨ Design Context Awareness
Upload a screenshot of your existing product, and VZero will generate matching UI components. It mimics style guides, layouts, and brand consistency.
🧩 Use Cases:
Prototyping admin dashboards
Mocking audit interfaces
Creating forms for automation workflows
Built on modern React/Next.js frameworks, it outputs usable code for immediate integration.
AI’s Impact on Productivity and Efficiency
The cumulative impact of these AI tools on DevOps workflows is significant. What used to take entire teams months to complete can now be accomplished by a single engineer within weeks, thanks to AI-driven automation and structured project management. The cost-effectiveness of these tools is also noteworthy; a typical monthly subscription to all mentioned AI tools averages around $70. Given the efficiency gains, this represents a valuable investment for both individual professionals and organizations.
How to Use AI in DevOps Without Sacrificing Quality
To maximize AI’s potential, DevOps professionals must go beyond simple code generation and understand how to fully integrate these tools into their workflows. Successful use of AI involves knowing:
When to rely on AI versus manual coding for accuracy and efficiency.
How to assess AI-generated results critically to avoid errors.
The importance of providing comprehensive prompts and reference materials to get the best outcomes.
To maximize value:
🔍 Review AI output like you would a junior developer’s code.
🧠 Prompt engineering matters—give context, not just commands.
⚠️ Don’t outsource critical logic—review security and environment-specific settings carefully.
By mastering these skills, DevOps teams can ensure that AI tools support their goals effectively, adding value without compromising quality.
Conclusion
AI tools have become indispensable in DevOps, transforming how engineers approach their work and enabling them to focus on higher-level tasks. As these tools continue to evolve, they are likely to become even more integral to development operations, offering ever more refined support for complex workflows. Embracing AI in DevOps is no longer a choice but a necessity, and those who learn to use it wisely will enjoy substantial advantages in productivity, adaptability, and career growth.
If you’re not leveraging AI in DevOps yet, you're falling behind.Want to scale your DevOps efficiency with AI-backed automation?Connect with Gart Solutions to modernize your pipelines today.
DevOps - it's just a marketing buzzword with no substance behind it, right? Or perhaps there's more to it than meets the eye. Is DevOps simply a collection of "correct" tools, or is it a specialized culture? And who exactly should be responsible for it, and what does a DevOps engineer entail?
In short, there are some misconceptions abound, some trivial, some deeply rooted in the minds of respected professionals.
Myth #1: DevOps Can Be Done by a DevOps Department or Engineer
To achieve proper DevOps, we hire DevOps engineers or create a DevOps department, and voila! We've got full-fledged DevOps in our company!
This sentiment is quite pervasive. Despite numerous materials explaining why there's no such thing as DevOps engineers or why creating a DevOps department isn't advisable, DevOps departments still emerge, and DevOps engineer vacancies continue to increase.
Here's a scenario I often encounter: a typical company where nothing much happens until the CEO or the board catches the "digital transformation" bug. It's a highly contagious disease usually contracted at conferences for CEOs or top management. Suddenly, terms like Agile, DevOps, digital, product, etc., start popping up in their heads. The CEO gathers the troops and says, "Guys, we need Agile, DevOps, digital, figure it out and make it happen."
In reality, it's not the CEO's task to delve into all of this. They've figured out, for instance, how to increase revenue, value streams, and realized that it correlates with these buzzwords.
Then the team starts brainstorming. They read about DevOps on Wikipedia: "A methodology that unifies developers, operations, and testing for continuous software delivery."
In their company, software delivery is continuous, deployments happen every three months, developers and sysadmins hang out together at bars - there's no issue with collaboration. So, they decide to rename some department to DevOps.
Ultimately, either the quality assurance department gets renamed to DevOps or the operations department - depending on the experience of the individuals involved. But what to do with that department if it's filled with quality/issue engineers? They need to be renamed to DevOps engineers too?
I regularly browse through DevOps job listings because we and our clients are looking for engineers. You can check out job search websites yourself; they're all so diverse!
There's no strict standardization or typification: from people setting up pipelines on Jenkins to testers writing automation scripts; from specialists deploying on AWS to those configuring monitoring, rolling out software, etc. Every possible IT profession is labeled as a DevOps engineer.
The exception is small companies genuinely producing digital products with real continuous software delivery. They seek people who can do it all because it's hard to pinpoint a specific qualification. So, they simply advertise for a DevOps engineer position listing everything - Jenkins, Ansible, Chef, Docker, Kubernetes, Silex, Prometheus.
The most crucial aspect of needing DevOps is to create value flow throughout the company. DevOps should cover everything from analytics to actual operations. It's not a separate profession or a designated role; it's merely a concept that facilitates rapid digital product delivery.
The first mention of this term dates back to 2009 when Patrick Debois said at a conference: "We've started using a new approach. We're now producing digital products, and we have a problem - the old ways aren't working. I alone can't solve it. I invite everyone to join the community to figure out how to live with this. Let's call this problem DevOps."
DevOps is the solution to the problem of producing digital products. It's a methodology. Calling an individual by that term is incorrect.
If you're seeking a DevOps engineer, consider why you need one. It's better to specify a particular skill set and look for it in the market than hire an ambiguous specialist.
For the financially prudent: typically, a system administrator with Docker knowledge differs from a DevOps engineer with Docker knowledge not in skills but in price.
Myth #2: DevOps is about Hiring Jack-of-All-Trades Specialists
Let me share my interpretation of where this problem stems from. When the whole DevOps saga was just beginning, indeed, all the work was done by jack-of-all-trades specialists. For example, one person would build the delivery pipeline, set up monitoring, and so forth. I started from the same point and did everything myself because I didn't know whom to delegate tasks to: I needed to research here, tweak there, and implement something else. As a result, I became a jack-of-all-trades specialist who configured Linux, wrote Chef scripts, integrated with Zabbix API, and more. I had to do it all.
But in reality, this doesn't work.
Productivity:
18 jack-of-all-trades craftsmen capable of making a pin each produce 360 pins per day.
18 craftsmen divided into 18 specializations produce 48,000 pins.
This is a well-known example of production increase in a pin factory from Adam Smith's book. In reality, the story about these pins wasn't invented by him but rather borrowed from a British encyclopedia.
This logic still holds true today. When I hear that specialists should be cross-functional, I actually think that teams should be cross-functional, but certainly not individuals. Otherwise, we end up needing to produce 48,000 pins but simply don't have enough people.
In the book "The Phoenix Project: A Novel about IT, DevOps, and Helping Your Business Win," there's a story about Brandon, who does everything in the company, and everything bottlenecked around him. This is precisely the story of a talented jack-of-all-trades specialist.
But then the question arises - if not hiring a jack-of-all-trades specialist, then whom to hire? There's no clear answer to this question. We don't specify specific specializations for people working in DevOps.
I'll try to outline these roles broadly and invite you to join this work because without role division, we'll always be talking about jack-of-all-trades specialists.
DevOps, other roles:
Developer with an understanding of architecture and production software work (writes tests and infrastructure code)
What sets this developer apart from a typical university graduate? A developer from a typical university can write algorithms - and that's about it. A DevOps developer not only can write descriptions but also knows how these descriptions come to life.
I came to IT from radio electronics, and what amazed me here was that people think that a schematic diagram is a working product. An engineer in radio electronics doesn't have such a misconception, but in IT - it exists.
We need developers who know that a schematic diagram is not a working product. There are few of them; they come from related fields - like radio electronics.
Infrastructure Engineer (writes bindings for infrastructure, provides a platform for developers)
This role is so rich that it can be further broken down. Essentially, it includes a product owner manager who owns the continuous delivery platform product and the individual competencies of people who build this delivery platform. But this is for large companies.
For small companies, it's simpler. This is an engineer who is well-versed in configuration management, knows Docker, Kubernetes, can create a continuous software delivery process based on them, and hand it over to the developer.
Developer of infrastructure services (DBaS, Monitoring as a Service, Logging as a Service)
These are services provided to developers as a product. Note that you can go to Amazon and buy these individual services. Amazon can be considered a DevOps role model; that is, what they sell can be grown internally.
Release Manager (manages the process and dependencies)
This isn't the person who builds the build. They manage dependencies, versions, facilitate team agreements on integration environments, oversee the continuous delivery pipeline, and so forth.
They are the fairy who endows your team with magic so they can continuously deliver software.
Myth #3: We've Been Doing DevOps Since 1995
"We've been developing our corporate IT system for many years and have been releasing every week. Your DevOps is just a bunch of millennials in jeans with rolled-up cuffs who came along and renamed what we've been doing for years."
This myth stems from a misunderstanding of what DevOps truly entails and a misinterpretation of the time-to-market concept. Some organizations believe that because they release software frequently, they are already practicing DevOps. They may view DevOps as simply a rebranding of their existing processes rather than a fundamental shift in approach.
The reality is that DevOps is not just about the frequency of releases; it's about the entire software delivery lifecycle, from development to operations, being optimized for speed, efficiency, and collaboration. While releasing every week is a positive achievement, true DevOps involves more than just release frequency.
One of the key principles of DevOps is continuous improvement, which means constantly refining and optimizing processes to deliver value to customers faster and more reliably. It's about breaking down silos between development, operations, and other teams, fostering a culture of collaboration and shared responsibility.
Another misconception related to this myth is the idea that DevOps only becomes relevant during incidents or emergencies. While it's true that DevOps practices can help teams respond more effectively to incidents, they are equally important during normal operations. DevOps is about building resilience and reliability into the software delivery process from the outset, not just reacting to problems as they arise.
Ultimately, DevOps is about delivering software that meets customer needs quickly and efficiently. It requires a mindset shift and a commitment to continuous improvement, rather than simply sticking to existing processes and routines.
While frequent releases are a positive step, true DevOps involves a holistic approach to software delivery that goes beyond release frequency and encompasses the entire lifecycle of software development and operations. It's not just about what you've been doing for years; it's about how you can do it better.
DevOps Practices for TTM (Time-to-Market)
I've outlined some core DevOps practices focused solely on time-to-market, without which continuous feedback from your customers and the market is unattainable.
Release Strategies (blue-green deployments, canary releases, A/B testing):It sounds good and quite simple — let's roll out to 0.1% of users. But to do this requires monumental effort, such as transitioning from a monolithic architecture to a microservices architecture.
Continuous Monitoring:This is monitoring akin to testing, where developers describe, in code, what needs to be monitored in production. Afterward, the artifact, along with the description of what needs monitoring, is pushed through the pipeline, and everything is added to monitoring at every stage of continuous delivery. This allows a real look at what's happening with the application.When you've deployed to 0.1%, you can understand if the software is working as intended or if something is amiss. It's pointless to deploy to 0.1% if you don't know what's happening with the software.
End-to-End Logging:This is when, via an ID, you can see in the system how customer data has changed: they come in through the frontend, then to the backend, then to the queue, where they're picked up by an asynchronous worker. The asynchronous worker then goes to the database, changes the cache state, and all of this is logged. Without this, figuring out what happened in the system is impossible, and deploying to 0.1% is futile.Imagine you decide to deploy some new software to all 2,000 attendees of the "International Internet Technologies" festival, but you lack end-to-end logging. These 2,000 people complain that it's not working. You ask them what's wrong because from your perspective, it's like some kind of poltergeist. It's a futile endeavor.
Automated Tests (as a form of agreement between teams):Automated testing has long been known and used for improving software quality. However, automated tests need to be approached differently — as a form of agreement between teams. Instead of resolving numerous small issues in the software during integration, teams agree that each team writes automated tests and addresses these issues at the stage of developing individual modules, elements, etc. Writing automated tests is a separate story.
All these described components — DevOps practices for TTM — require a lot of time and effort if you're not starting from scratch, but rather, for example, rewriting a monolith. However, without them, achieving a high time-to-market is impossible.
Myth 4: DevOps is all about the "right" tools
There's a common belief that DevOps can be achieved simply by adopting the "right" tools. It's like assembling a toolkit with Kubernetes, Ansible, Prometheus, Mesosphere, and Docker, and expecting instant DevOps success.
However, this approach often leads to disappointment. Organizations might install Kubernetes, for example, use it for a while, and then realize it's not as effective as they hoped. They find themselves grappling with too many components, encountering glitches, and realizing that manual processes might have been simpler.
While powerful tools are certainly valuable, they're just one piece of the DevOps puzzle. True DevOps isn't just about the tools you use; it's about the cultural and process changes within your organization.
That said, there are indeed tools specifically designed for DevOps, created by the community that emerged around the DevOps movement. These tools come with not only installation instructions but also comprehensive guides on how to leverage them effectively.
For instance, when you install Git, you don't need to understand the intricacies of hashing algorithms. Similarly, with Kubernetes, it's more important for engineers to understand how to work with it rather than the internal workings of the platform.
While the "right" tools are important, they're not a silver bullet for DevOps success. It's crucial to focus on building a culture of collaboration, automation, and continuous improvement alongside tool adoption.
Myth #5: DevOps is only for large organizations
DevOps can benefit businesses of all sizes. Smaller companies may even find it easier to adopt a DevOps culture due to their agility and fewer layers of bureaucracy. DevOps principles like continuous integration and delivery can be implemented incrementally, even with limited resources.
Myth #6: DevOps is only for startups or tech companies
While DevOps originated in the tech industry, it is not limited to startups or tech companies. DevOps practices can be adopted by organizations of any size and across various industries to improve software delivery and operational efficiency.
Myth #7: DevOps requires a complete overhaul of your workflow
DevOps is about continuous improvement, not starting from scratch. You can begin by focusing on small changes that promote collaboration and automation. Gradually, your development and operations processes will become more efficient and streamlined.
Myth #8: DevOps replaces traditional IT roles
DevOps does not replace traditional IT roles like developers, system administrators, or quality assurance professionals. Instead, it promotes cross-functional collaboration, shared responsibilities, and a culture of continuous learning and improvement.
Myth #9: DevOps is a one-time implementation
DevOps is a cultural shift, not a one-time fix. It requires ongoing commitment and adaptation. As your organization and technology landscape evolve, your DevOps practices will need to evolve as well.
Myth #10: DevOps is only for cloud-native or microservices architectures
DevOps practices can be applied to various types of software architectures, including monolithic, service-oriented, or cloud-native applications. The principles of collaboration, automation, and continuous improvement are relevant regardless of the underlying architecture.
By understanding and addressing these myths, organizations can better embrace DevOps as a cultural shift and a set of practices that foster collaboration, automation, and continuous improvement in software delivery and operations.
Substantial Time and Cost Benefits
