DevOps is an approach that reconciles development and operations. Widely used in the software development field, it is also embraced in the realm of Data Science and Machine Learning. Explore everything you need to know: definition, principles, tools, history, training, and more.
In the past, development and operations were separate entities in the software field. Developers wrote the code, and system administrators were responsible for its deployment and integration.
Communication between these two silos was limited, and specialists in each domain worked separately on the same project.
This operational mode was satisfactory when the Waterfall development method was predominant. However, with the rise of Agile methodology and continuous workflows, a change became necessary.
Frequent releases, occurring every two weeks or even daily, demanded a new approach and new roles. This is how the DevOps approach came into being.
Today, this software development approach is the most popular, used by Facebook, Netflix, Amazon, Etsy, and other globally renowned companies. Discover everything you need to know about it.
What is DevOps?
The term “DevOps” is composed of the words “development” and “operations.” It represents a practice aimed at merging development, quality assurance, and operations, including deployment and integration, into a single set of continuous processes.
DevOps is a combination of cultural philosophies, practices, and tools designed to enhance an organization’s ability to deliver applications and services at high velocity.
The adoption of DevOps enables the evolution and improvement of products at a much faster pace than traditional software development and infrastructure management processes. As a result, organizations can better serve their customers and compete with greater efficiency.
Under the DevOps model, development and operations teams no longer operate in silos. They can even form a unified team in which engineers work on the entire application lifecycle, from development to deployment, including testing.
Security and quality assurance teams can also collaborate with development and operations teams. When security is a DevOps team’s top priority, it’s referred to as DevSecOps.
Teams use practices that automate processes that were once manual and slow. Various tools and software stacks assist in rapidly and reliably evolving applications, while helping engineers accomplish their tasks independently.
What are the benefits of DevOps?
By adopting DevOps, companies can reap numerous advantages, which is why this approach is widely embraced and often considered the future of IT.
First and foremost, the primary benefit of DevOps is speed. This method accelerates product launches and improves their quality. Speed is closely tied to continuous delivery, enabling faster feedback, which in turn allows developers to identify and rectify bugs early in the process. This means teams can focus on product quality and automate processes.
The second advantage of DevOps is increased responsiveness to customer needs and demands. Teams can react more swiftly to customer change requests, adding new features or updating existing ones.
The enhanced velocity allows for faster innovation, adaptation to market changes, and more efficient achievement of company objectives. For instance, microservices and continuous delivery empower teams to deploy updates more rapidly.
Another strong point is reliability. DevOps ensures the quality of application updates and infrastructure changes, enabling accelerated delivery while maintaining a positive user experience. CI/CD practices, for instance, involve testing every change to ensure functionality and security. Real-time monitoring also helps in monitoring performance.
The DevOps model keeps a high level of security through automated compliance rules, granular controls, and configuration management techniques. For example, infrastructure as code helps maintain compliance.
Lastly, DevOps creates a better working environment. Team members can communicate more effectively, while their productivity and agility are enhanced. In general, teams adopting DevOps are more productive and versatile.
The history of DevOps
The origin of DevOps is closely tied to the need for innovation in software development. It is an inheritance from the Agile System Administration and Enterprise Systems Management movements.
The concepts of DevOps gained popularity in the late 2000s. However, it was in 2009 that the term was truly coined by Patrick Debois and Andrew “Clay” Shafer. The first DevOpsDays event was organized in Belgium, in Ghent, marking a significant milestone in the development of the DevOps movement.
What are the principles of DevOps?
To harness the benefits of DevOps, it’s important to understand that it’s not merely a set of actions but rather a philosophy. The idea is not to make technical changes but to alter how teams work together.
DevOps primarily relies on a set of principles. In 2010, Damon Edwards and John Willis summarized these principles with the acronym “CAMS”: Culture, Automation, Measurement, Sharing.
First and foremost, it’s a culture, a collaborative mindset between development and operations teams. This culture is built on constant communication and collaboration, gradual changes, shared responsibility, and early problem resolution.
The second principle is systematic automation of development, testing, configuration, and deployment procedures. Whenever automation is feasible, it should be embraced to eliminate repetitive and time-consuming tasks, allowing teams to focus on important activities that cannot be automated.
Measuring Key Performance Indicators (KPIs) continuously tracks the progress of activities within the DevOps flow. This enables data-driven decision-making, understanding what works and what doesn’t, and optimizing performance.
Finally, sharing is essential. Teams must share feedback, best practices, and knowledge to promote transparency, create collective intelligence, and remove constraints.
DevOps practices and model
The philosophy and principles of DevOps are applied through a delivery cycle model that includes planning, development, testing, deployment, release, and monitoring. Throughout these stages, active collaboration among team members must be continuous.
Planning should be agile. Work is organized around short iterations known as “sprints.”
This increases the frequency of releases and intensifies their pace. In practice, only high-level objectives are set, while teams plan in advance and in detail for one or two iterations. This approach offers flexibility.
The concept of continuous development is also based on an iterative approach. All development work is broken down into small portions for faster and higher-quality production. Engineers contribute to the code in small increments multiple times a day to facilitate testing.
Testing is also continuous and automated. A quality assurance team tests the code using automated tools like Selenium and Ranorex. If bugs or vulnerabilities are discovered, code snippets are sent back to the engineers. Version control also helps detect integration issues in advance.
If the code passes the tests, it is integrated into a single shared repository on a server. This prevents differences between the main code and its branches to avoid integration problems. This is the concept of continuous integration. On the other hand, continuous delivery involves automating code development, testing, and deployment.
Next comes the stage of continuous deployment. The code is deployed to run in production on a public server, without affecting existing features and making it accessible to a wide range of users. Frequent deployment allows for early testing of new features.
Various tools like Chef, Puppet, Azure Resource Manager, or Google Cloud Deployment Manager are used for this purpose.
Finally, the last stage of the DevOps cycle is continuous monitoring. This involves continuous project monitoring to detect potential issues in a process and analyzing feedback from the team and users to improve the product’s functionality continually.
What are the DevOps tools?
To implement DevOps practices, it’s necessary to use various tools to cover all stages of the continuous delivery process.
While some processes are automated using custom scripts, most DevOps engineers rely on dedicated products.
For server configuration and management, tools like Puppet, Chef for infrastructure as code management, or Ansible for automating configuration management, cloud provisioning, and application deployment are commonly used.
For continuous integration and continuous delivery (CI/CD) stages, Jenkins and its plugins, as well as GitLab CI, created for DevOps by the GitLab code hosting service, are widely used. Docker is the most popular tool for containerization, while OpenShift and Kubernetes are employed for container orchestration.
There are also monitoring tools in the DevOps toolkit, such as Nagios with its visual reports or the open-source solution Prometheus. All of these platforms together make up a comprehensive arsenal for implementing the DevOps methodology.
What is a DevOps engineer?
While there isn’t a consensus on the exact definition of the DevOps Engineer role, this expert is highly sought after in the field of IT. Their role involves overseeing developers, the quality assurance team, code release managers, or automation architects.
One could define them as a role that bridges the gap between a software developer and a system administrator. They have expertise in both the theoretical aspects of DevOps and the various associated tools and programming languages.
A DevOps Engineer manages CI/CD processes, writes specifications and documentation for server-side features, supervises projects, handles infrastructure, takes care of cloud deployments, and ensures that the DevOps culture is properly embraced.
The future of DevOps
DevOps has proven its worth, enabling the acceleration of development processes and the improvement of product quality. Looking ahead, several changes are on the horizon.
As many companies migrate to the cloud, DevOps will be intricately linked to cloud-native security. The way software is developed, deployed, and operated will evolve in this direction. This is often referred to as “SecDevOps,” which integrates security into development and deployment workflows.
Some experts also predict the democratization of “BizDevOps,” aiming to eliminate boundaries between developers, operations teams, and business teams. This approach will enable the faster development of user-oriented products.
Lastly, development teams may become more involved in decision-making aspects, helping companies steer in the right direction. This increased collaboration can lead to more informed and effective choices.
DevOps and Data Science
DevOps is increasingly being applied in the field of Data Science. In Data Engineering, particularly, Data Engineer teams must collaborate with DevOps teams to automate data transformation. Operators provide clusters for Apache Hadoop, Kafka, Spark, and Airflow for data extraction and transformation.
Likewise, DevOps teams assist Data Scientists by creating environments for data exploration and visualization. They also create scripts to automate provisioning and configuration for Machine Learning model training infrastructures.
Machine Learning development follows an iterative process, similar to modern application development. Machine Learning models created from data evolve and need to be made available to users through DevOps and CI/CD practices. Each version of the model is packaged as a differently labeled container image.
In general, DevOps is used in Data Science for source control, test automation, containerization, and security. Data Scientists must, therefore, collaborate closely with DevOps teams.
Nowadays, Data Scientists are transitioning into a new role: that of Machine Learning Engineer. They must be capable of deploying Machine Learning models into production themselves, which necessitates adopting DevOps practices.
The impact of Machine Learning and AI on DevOps
The application of Artificial Intelligence (AI) and Machine Learning (ML) to DevOps is still in its early stages, but organizations can already benefit from these technologies.
AI and ML can make sense of test data, helping to identify patterns, pinpoint which code issues lead to bugs, or automatically alert DevOps teams for in-depth investigation.
Moreover, DevOps teams can use AI and Machine Learning to analyze security data from logs and other tools, detecting leaks, cyberattacks, or any other threats. These technologies can also automate responses and send alerts to the teams.
Developers and ops can save time, as AI learns how they work and can make suggestions to optimize workflows. It can also automatically provision their preferred infrastructure configurations.
DevOps and CI/CD
Continuous Integration and Continuous Delivery (CI/CD) are fundamental elements of DevOps and any other modern software development practices. A CI/CD platform optimizes development time, improves productivity, enhances efficiency, and streamlines workflows through automation, testing, and collaboration.
As the size of applications grows, CI/CD can help reduce development complexity. Other DevOps practices complement this approach, aiming to eliminate development silos and simplify scaling.
How do you learn about DevOps?
A DevOps training not only opens doors to the role of a DevOps Engineer but can also be incredibly valuable for a software developer or a Data Scientist. With the training offered by DataScientest, you can gain skills in data science and DevOps.
DevOps practices and tools are at the core of our training programs, especially in the Data Engineer and Machine Learning Engineer tracks. You will learn how to use automation and deployment tools like Docker, AirFlow, Kubernetes, and Gitlab’s DevOps platform.
