Being part of the global digital economy, every company is obliged to follow the only universal rule: change is the only constant. In today’s world, software became the primary mechanism of delivering value to customers and generating revenue for corporations. That is why the classical engineering paradigm doesn’t meet modern business needs anymore. No company can afford spending months writing, testing, and debugging software. Organizations need to increase speed of software delivery and make sure about its high quality.
Therefore, DevOps became the key to success. Through merging of software development and IT operations into a single automated process, DevOps eliminates existing bottlenecks that prevent increasing software engineering efficiency. Companies cannot afford to update software once per year anymore; instead, they have to switch from annual software updates to its continuous improvement.
When properly implemented, DevOps revolutionizes the entire software engineering process. Thanks to DevOps, companies can integrate and deploy changes in code developed on the developer’s laptop to the cloud production environment within minutes instead of months. Understanding how DevOps achieves this goal is essential to grasp the concept.
1. CI/CD Pipelines: The Ultimate Continuous Delivery Framework
The most important feature that makes it possible to deliver high-quality software fast is CI/CD pipelines. In the classical engineering paradigm, integration is a long process when engineers spend months writing isolated code pieces and adding features to the codebase.
Eventually, when engineers start to integrate code changes, they come across the so-called “merge hell” – numerous syntax errors, architectural inconsistencies, and logical flaws.
However, DevOps eliminates this bottleneck through the continuous integration process. Instead of updating code monthly, engineers merge code changes into the common repository constantly. As soon as engineers commit a piece of code, CI server builds an application and performs unit, integration, and regression testing to make sure that software works as expected.
If there are any issues in code changes (bugs or architectural inconsistencies), testing will detect this problem instantly, providing engineers with feedback. It allows engineers to fix the problem timely and avoid application crash.
Moreover, there is an approach called Continuous Delivery when as soon as software passes automatic testing, the CI pipeline packs the application and prepares it for deployment. Some advanced DevOps practices also include Continuous Deployment where code changes go straight from testing to production automatically.
2. Shift-Left Testing and Continuous Quality Assurance
One of the most spread myths created by traditional project managers is that fast software delivery leads to poor quality of software. However, DevOps shows how this myth is wrong using shift-left testing. In the traditional software development paradigm, quality assurance checks and security reviews were the final steps in the release pipeline. If there were any vulnerabilities found, engineers had only two choices: postpone the release or deploy the application.
The shift-left testing approach involves conducting quality assurance checks and reviewing the code in the very beginning of the development process. As soon as engineers add some code, static code analysis tools like SAST detect insecure code patterns and potential vulnerabilities in the software.
Additionally, software composition analysis (SCA) tools check whether engineers use vulnerable libraries in their projects.
Finally, engineers conduct end-to-end testing simulating actual user experience with different browsers and operating systems.
Such quality assurance techniques make it possible to identify potential problems when the code is fresh in the memory of engineers, thus saving lots of time and effort. Moreover, it reduces the number of regressions in production software and guarantees high quality of every application deployed.
3. Infrastructure as Code and Environment Consistency
Another bottleneck in the software engineering process that exists even before DevOps was the issue of managing cloud resources. Before DevOps, engineers had to set up cloud infrastructure manually by navigating cloud provider console and configuring operating systems, networking, etc.
There were two major downsides of this approach. First, it was too labor-intensive and prone to human errors. Second, engineers couldn’t ensure that development, staging, testing, and production environments were consistent and identical, which led to the cliche of a software engineer: “It works on my local machine.”
With DevOps, organizations got an ability to automate infrastructure management using Infrastructure as Code approach. Thanks to infrastructure configuration tools like Terraform, OpenTofu, and Pulumi, systems engineers can manage cloud resources (networks, database clusters, firewall rules, etc.) using plain-text configuration files.
These files can be committed to a version control system like Git along with other software components. When engineers need to build a new environment for testing purposes, CI pipeline takes the configuration file and sets up cloud infrastructure according to it.
Such automation not only increases software delivery speed but also makes cloud environments identical. The application running on a local machine will behave exactly the same way on the staging and production servers because configuration of cloud resources will be identical.
With IaC, DevOps improves software delivery process, prevents configuration drift, and enables on-demand cloud infrastructure scaling.
4. Microservices Architecture and Ephemeral Environments
In order to achieve the maximum software delivery velocity and software quality, DevOps often goes hand-in-hand with transition to microservices architecture. In the classical engineering paradigm, applications consist of large and tightly coupled monoliths with interconnected code blocks, business logic, and database connections.
For example, if an engineer wants to change something in the UI (font or layout), he or she would need to rewrite the whole application.
The microservices approach makes it possible to divide software into separate services communicating via lightweight APIs. Each microservice implements a certain business functionality (e.g., payment processing, shopping cart operations).
Using containerization tools like Docker and cloud orchestration platforms like Kubernetes, microservices can be deployed, managed, and scaled separately from one another. This means that the failure domain becomes extremely small since engineers can isolate a particular microservice.
Additionally, microservices architecture makes it possible to deploy updates independently. Different engineering teams can deploy dozens of changes per day without coordinating with one another, making software delivery speed maximal.
5. Real-Time Observability and Instant Feedback Loops
However, DevOps is not only about deploying code into production. In order to continuously optimize software quality, modern engineering heavily relies on continuous monitoring and observability practices.
Previously, engineers knew about software issues only when the customer contacted them with a complaint. In other words, companies were waiting for the customer feedback to fix software issues.
However, with DevOps, companies implemented proactive feedback loops using automated monitoring solutions. Integration of monitoring tools like Prometheus, Grafana, and Datadog into the deployment pipeline ensures real-time observability of the system.
These tools collect system metrics, logs, and user activity data and use machine learning algorithms to determine normal system behavior and deviations.
For instance, the sudden increase in CPU utilization or decrease in API response time is instantly detected by monitoring tools, generating alerts for engineers.
Such real-time observability of the system allows addressing the problem immediately, saving money and minimizing downtime. Moreover, it helps prioritize future development efforts.
Frequently Asked Questions (FAQ)
What is the difference between DevOps and Agile?
Agile is a project management approach that seeks to tear down software engineering silos, cooperate with customers, and deliver software in sprints. DevOps can be considered as an evolution of Agile in the context of IT operations. Agile addresses planning and software development processes, while DevOps focuses on testing, provisioning, deployment, and monitoring.
How do automated testing pipelines save money?
Automated testing pipelines help to save money because they detect software issues early in the engineering process. The cost of fixing bugs exponentially grows throughout the engineering process. Bugs discovered at the development phase cost nothing, while those discovered in production may cost millions. Therefore, automated testing saves money.
Can organizations implement DevOps without moving to the microservices architecture?
Yes, they can. Even in the case of a monolith architecture, such DevOps practices as CI/CD, IaC, monitoring, etc. bring significant productivity benefits. However, microservices make full DevOps automation possible.
What is the role of containerization in DevOps quality?
Containerization provides consistency of the application runtime environment. Containerizing software means bundling the application into a self-contained environment sharing the same OS kernel with the host. Therefore, containers ensure that the application will work identically in all environments (development, staging, and production). Hence, it prevents environment-related issues.
What is GitOps?
GitOps is a continuous deployment approach where engineers consider the only source of truth to be a Git repository. Instead of deploying software using automation scripts, engineers commit changes to a Git repository. There is an operator in the target cloud environment that observes the Git repo and applies code changes instantly.
