Who would not want to become a DevOps engineer?   

The global talent shortage shows no signs of easing, and Devops is one of the most in-demand skills for the foreseeable future. The DevOps market will be worth USD 14.9 billion by 2026, with a CAGR of 19.1%.  

DevOps presents exciting job prospects, opportunities to work on innovative projects, and the potential to acquire versatile skills. Here is how to go about the task of developing the necessary competencies and expertise for DevOps. 

 

1. Have clarity on what Is DevOps

First things first. It is essential to have clarity on what exactly DevOps means before getting into the nitty-gritty of how to develop a career out of it.  

DevOps is a collaborative approach to software development and operations, where developers and operations engineers work together to achieve a common goal.   

Traditional software development keeps the work of developers and operations distinct. The two teams have little understanding of each other’s work. The operations team focuses on stability, and developers have an ultimate goal of change, leading to friction. Since operations resent disruptions, developers update the codes infrequently. Since major changes accumulate during the period, updates cause significant downtime.  

DevOps promotes a cohesive partnership between developers and operations engineers, in which each team shares responsibilities. They use automation and rapid testing to deliver frequent updates, sometimes multiple times daily. Frequent, minor changes minimize downtime, improve software quality, and speed up time-to-market.  

DevOps is more of a culture or “a way of working” than any specific role. Many companies take such a concept literally and do not have Devops Engineers. They instead promote the DevOps culture among their infrastructure engineers, systems reliability engineers (SRE), CI/CD engineers, sysadmins, and other engineers.   

The role of a DevOps engineer  

The everyday DevOps tasks, regardless of the job title or job description, include:  

 

• Automating infrastructure deployment  
• Creating CI/CD pipelines  
• Configuring container orchestrators 
• Collaborating with developers to design microservices architecture. 

• Planning DevOps strategies for infrastructure and deployment.  
• Designing monitoring and observability platforms and providing recommendations to various in-house teams.  
• Offering advice and recommendations on infrastructure and platform to development teams.  

• Ensuring the security of workloads and infrastructure. 
• Implementing agile and lean in the workplace.

1. Develop the core technical skills 

It is not possible to learn DevOps technical skills without a basic understanding of some core skill sets.   

1. Linux (OS fundamentals)

It is possible to focus on Windows and follow an exclusive Microsoft path in the DevOps journey. But the vast majority of DevOps roles make use of Linux in some form. 

Countless online resources make learning Linux easy. RedHat RHCSA​ is a widely recognized certification to prove competence in Linux. Other Linux certifications include Comptia Linux, LPIC I, and ​LPIC II​.  

1. Basic programming skills

DevOps does not require advanced programming knowledge. But there is no workaround to a good understanding of basic programming concepts. DevOps engineers have to read and modify scripts that automate workloads. Knowledge of concepts such as loops, variables, arrays, and lists is essential for the task.   

 There is no strict recommendation for a particular language for DevOps. Python is the most popular and recommended option for beginners though. Those looking to advance beyond the basics, need to possess knowledge of standard design patterns and algorithms.  

Beginners in programming would do well to delve into Python Free Interactive Tutorial​. They would also find Edabit and Hackerrank programming challenges websites handy. Ideally, start with easy tasks and work the way up to intermediate, to develop competencies in scripting and automation. The Python Institute offers Python Certifications.​  

1. Bash

Bash is the essential scripting language to automate tasks in the Unix environment. Knowledge of Bash is valuable even when Phyton is the scripting language of choice.  

Additional resources include Beginner Bash Guide, Advance Scripting Guide, ​Bash interactive tutorial, and The Bash Guide.  

1. DVCS such as Git

DevOps engineers need knowledge of Distributed Version Control System (DVCS). DVCS finds common use in software development workflows to manage code changes. DVCS also integrates with the Best DevOps tools such as CI/CD, to automate the development and deployment processes.  

DVCS systems such as Git offer several advantages over traditional centralized systems. DevOps engineers proficient in DVCS can manage the entire development process from code creation to deployment, with more efficiency and agility.  

Another Git resource is git — the simple guide​. Knowledge of Version Control​ will also be helpful. Learn git Branching is an interactive course with challenges.  

1. Networking fundamentals

Modern software development and deployment often involve complex distributed systems. These systems rely on various network technologies and protocols to communicate with each other.   

DevOps engineers need familiarity with 

• Essentials of networking such as protocols, routing, switching, TCP/IP, DNS, subnet and subnet masks, and load balancing. 
• Firewalls and security.  
• Network monitoring and troubleshooting tools to identify and resolve issues quickly and effectively.

These fundamentals enable the design and maintenance of efficient and reliable network infrastructures, thus making the deployment of complex applications easy.   

Numerous free online resources allow aspirant DevOps engineers to master network fundamentals. The Cisco Networking Academy offers free courses covering a wide range of basic to advanced concepts.   

1. Cloud platform fundamentals

Understanding how to configure, manage, and optimize cloud resources improves application delivery.  

Familiarity with the three major cloud platforms – AWS, GCP, and Azure is essential for mastering DevOps. AWS is the most widely used, and GCP is the most intuitive and easy to use. Azure is the best option for working in a Microsoft environment. Familiarity with all three gives a solid foundation to get started with DevOps.  

Popular resources on cloud platform fundamentals include Google Cloud Free Offering, Quicklabs, and AWS Free Tier.​ Some handy certifications include Cloud Digital Leader GCP certification​, GCP Associate Cloud Engineer​, GCP Cloud Network Engineer, AWS Certified Cloud Practitioner, and Azure Fundamentals.  

Mastering these fundamental skills is indispensable for entry-level DevOps roles.  

 

1. Develop DevOps Competencies

Having mastered the core technical skills, the next step is to become familiar with the Best DevOps tools and practices used in DevOps roles.   

1. Containers

Containers are integral to DevOps. Containers package and isolate running apps to make them more consistent and secure. A container runs the same way regardless of whether it is the host server or computer, making it predictable and secure.  

DevOps engineers need competence in:  

• Key container concepts including container images, registries, orchestration, networking, and security. 

• Containerization platforms such as Docker and Kubernetes, and containerization tools such as Docker-compose, Helm, and Istio. 

 

The Docker Certified Associate is a must-have certification. The Docker cheat sheet is another essential resource.  

1. Continuous Integration (CI), Continuous Delivery (CD), and Continuous Deployment 

Continuous Integration (CI), Continuous Delivery (CD), and Continuous Deployment (CD) enable DevOps teams to build, test, and deploy applications quickly, reliably, and at scale.  RedHat’s What is continuous delivery?​ is a primary resource to get started.  

Understanding CI/CD well also requires understanding git branching models, git, Trunk-based development (TBD), Deployment Automation, and Continuous Testing. Learning tools such as Jenkins or Gitlab CI help master CI and CD. Jenkins, the most widely used CI tool, is very customizable, flexible, and easy to integrate with other tools. This open-source tool has an extensive plug-in library and a large community, making gathering resources easy.  

But Jenkins is not very user-friendly, and Groovy, the associated language to write pipelines, is not very convenient either. GitLab CI offers a simpler alternative, but lacks the power of Jenkins. It has a much smaller plug-in library and is less customizable or flexible than Jenkins.  

1. Infrastructure as Code (IaC)

Infrastructure as code (IaC) involves provisioning servers, networks, storage, and other resources through code.   

DevOps engineers find IaC useful to:   

• Define and manage infrastructure resources. 
• Achieve repeatability, consistency, and scalability.  
• Manage version control of infrastructure changes.  
• Test infrastructure configurations. 
• Deploy infrastructure resources in a quicker, repeatable, and predictable way.  

• Carry out infrastructure automation by deploying and managing infrastructure resources across multiple environments. 
• Improve audits. 

IaC involves two types of operations:   

• Configuration management, to carry out infrastructure automation for the configuration of running infrastructure. For instance, running scripts to install updates. Best DevOps tools such as Ansible, Puppet, Saltstack, and Chef allow configuration management. Packer is a popular way to create server images that co-opt all the needed software and upgrades. The Cloud Deployment Manager helps with orchestration for GCP. 

 

• Orchestration to create resources and metadata. The platform-agnostic Terraform is the most popular orchestration tool. Second on the list is CloudFormation for AWS. 

 

Top learning resources include Learn IaC with Terraform​ and How Ansible works. The Terraform certification is also handy.  

 

1. Microservices Architecture

Microservices offers a distributed architectural approach to software development. It breaks down applications into loosely coupled, independently deployable services. These services communicate with each other through APIs.  

Traditional monolith applications have all components tightly integrated and within the same server. Upgrading these monoliths as the enterprise requirements change, takes time. Issues such as connectivity and latency arise, and considerable downtime becomes inevitable. Microservices architectures offer flexibility and suit the demands of present-day requirements. Deploying microservices helps DevOps engineers:   

• Upgrade independent components without application downtime. 
• Make regular, piecemeal updates to the architecture.  
• Scale different parts independently. 

• Distribute responsibilities across teams working in different components.  
• Isolate faults easily.  
• Isolate data for use cases such as GDPR compliance. 

 

DevOps engineers need a solid understanding of:   

• Basic microservices concepts such as service discovery, registry, API gateway, and load balancing. 
• Deploying, managing, and monitoring microservices-based applications in a containerized environment. 
• Configuring microservices-based applications to use different data stores and messaging platforms. 
• Implementing security and compliance measures in microservices-based architectures. 

• Incorporating microservices into the CI/CD pipeline. 
• Automating building, testing, and deploying microservices-based applications to enable automated deployment and testing.  
• How to monitor the performance and availability of microservices-based applications. 
• Microservices-based infrastructure components and platforms such as Docker, Kubernetes, Consul, and Istio. 
• Mastery of key approaches such as the microservices pattern​ and the twelve-factor application model.  

 

Software Architecture Monday is a great YouTube playlist to understand different concepts about microservices and other architectures. 

 

1. Container Orchestrators

Conventional containerization has single or multiple containers running on one server. Orchestrators create multiple replicas of servers and containers to handle increasing traffic.   

Microservices offer a host of advantages, though a bit complicated. Developers designing fully decoupled applications often trade code complexity for infrastructure complexity. Issues such as latencies, connectivity, and retries, often manifest when developing microservices applications.  

Using container orchestrators make it easier to run microservices.  

 

Container orchestrators:

Enable operating containers across one or more servers, enabling horizontal scaling of workloads.  

• Detect applications under strain to automatically scale or restart. 

Developers running microservices in container orchestrators have to:  

• Split and redesign each component. 
• Add latency, connectivity, data, and config layers. 
• Segregate the above elements from the running application.  

 

DevOps engineers prefer many orchestrators. Docker Kubernetes is the clear winner in popularity, though. Other tools include ​Kind, GKE, EKS, AKS, K9s, and k0s. A good understanding and competency in a few or all of these tools can prove useful to master DevOps.​ Certified Kubernetes Administrator​ is a good certification to have. 

 

1. Monitoring and Observability 

 

Monitoring and observability ensure system reliability, availability, and performance. 

 

Monitoring involves collecting performance data from CPU, memory and network traffic, and responses. It identifies “known knowns” and “known unknowns” to improve performance. DevOps engineers use monitoring tools to identify and pre-empt potential issues.  

 

Observability analyzes the behavior of software systems without accessing their internal workings. Instrumenting the system with logs, metrics, and traces offers visibility into system behavior. It discovers “unknown unknowns” by learning new issues with richer data. 

 

DevOps engineers with competence in monitoring and observability can optimize system performance and ensure the smooth functioning of the system.  

 

The main techniques used in monitoring and observability are: 

• Logging or recording system events. 
• Metrics or time series that measure the number of events in a particular period. These are based on logs or system status such as RAM.  

• Service level indicators (SLIs) based on metrics, reflect the system’s current state.  
• Service level objectives (SLOs) ensure the system runs below a defined metric.