What is DevOps?

What is DevOps?

DevOps is a collaborative approach that brings together development and IT operation teams to collaborate for high-quality software deliveries. The idea is to offer improved collaboration between development teams and operations teams to ensure speed, security, and autonomy in software development.

With its market evaluation reaching beyond USD 10 million in 2023, DevOps has proven to be the most trusted way for businesses to introduce automation into their SDLC while maintaining agile development standards.

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Core Principles of DevOps

The core principles of DevOps are built around ensuring automation, collaboration, and agility around the SDLC. Here’s how these principles are defined:

• Collaborative effort: When silos are eliminated between development and operations teams, the business processes can run on a culture that encourages transparency and communication. With DevOps-initiated collaboration, problems can be solved faster, workflows can run more smoothly, and SDLC can improve consistently.
• Automation-friendliness: Any SDLC involves a set of repeatable processes for building, testing, and deploying the software. Automating these tasks is essential to ensure risk-free and continuous development. This makes automation a core principle for DevOps.
• Agility: Agile software development is by far the most trusted project management framework. Therefore, to ensure its continued emphasis in the SDLCs, DevOps has its principles aligned with the Agile framework.
• Consistency: DevOps processes tend to cover the entire SDLC end-to-end to ensure smooth workflows. It brings continuous integration, continuous delivery, continuous deployment, and continuous testing among other such features to ensure that there is an uninterrupted refinement of the code as per agile requirements.

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How DevOps works: The DevOps lifecycle

With the above-listed principles, DevOps strategies are built to reap practical benefits for software development projects, irrespective of the complexities they might face. Here are the steps to make DevOps work:

• Agile planning for the development lifecycle: Before anything else comes, there is an emphasis on agility. The user stories, springs, features list, improvements, and more are planned per the requirement assessments. There is a conscious effort to look for spaces where automation and collaboration can be implemented naturally. 
• Development Process and CI/CD: Continuous integration and continuous delivery ensure that even within the development teams, there’s no interruption. Coders working on different components of the code can develop and deploy in their own time without having to worry about disrupting any other part of the code.
• Testing: Over the years, the software testing process itself has gone through automation strategies of its own. A lot of credit for this goes to the popularity of DevOps that’s been making space for features like continuous testing and shift left. Thanks to DevOps the functionality, security, and performance of the code can now be test at any point in the SDLC.
• Release and deployment: As the features are built, the CI/CD pipelines ensure that they are automatically deployed with minimal to no manual interventions. The code goes from staging to production, and the entire agile development process is sped up exponentially.
• Operations: Of course, DevOps is not just development. Once the code is deployed, the monitoring tools take over for the ‘Ops’ part. The application’s performance is monitored in real-time and issues are promptly resolved as soon as they are detected. 
• Feedback: DevOps also accommodates user feedback and feedback data that comes from monitoring tools to make software interactions better and more consistent.

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What is the goal of DevOps?

The goal of DevOps is to implement an end-to-end software development process that does not suffer from the gap between development and operations. Therefore anything that can help streamline the SDLC workflows is welcomed in DevOps. DevOps comes with the intent of maintaining transparency for the business leaders and their respective teams, from writing the code to monitoring it post-production.

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DevOps Culture

With collaboration, transparency, and end-to-end ownership of the software development lifecycle, DevOps culture is all about creating a silo-less environment. Therefore, the DevOps teams embody this culture by ensuring agility, cross-functionality, automation-cognizance, and secure code delivery.

This culture is essential so that the necessary DevOps tools, including automation tools, communication resources, and CI/CD tools, are duly accommodated in the SDLC.

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Benefits of DevOps

DevOps has its benefits rooted in the focus it offers on collaboration, automation, continuity, and agility. Here’s how these benefits are manifested:

• Development and delivery speed: With improved collaboration and CI/CD features, DevOps makes software delivery much faster. It essentially maximizes the potential of the agile framework by delivering the iterations within a short time without compromising on quality.
• Testing benefits: Testing in DevOps is preferably automated with features like continuous testing and shift left. This also ensure continuous improvement in the application and reduced downtimes.
• Security benefits: Even with faster time to market, DevOps does not compromise on security or any other aspect of software quality. The emergence of DevSecOps (DevOps with integrated resources for security assurance) is a testament to DevOps' continuous vigilance toward application security. ‍
• Innovation and customer experience: DevOps has made innovation easier for business leaders. From cloud to AI to IoT, DevOps always accommodates resources that can further encourage automating repetitive tasks and focusing on more nuanced aspects of customer experience.

How to Adopt a DevOps Model?

Adopting DevOps requires organizations to bring it, first and foremost, at a cultural level. This is the most challenging shift as it emphasizes collaboration between the siloed teams like development, testing, and operations, among others. Post that bringing in the automation tools, in areas like testing, code integration, and code deployment shouldn’t be that difficult.

The resources for continuous feedback and real-time monitoring would also require training of the workforce before their integration into the SDLC. Overall, a mindset of shared responsibility across the SDLC is what would keep the DevOps mode on within the organization.

Challenges of Adopting DevOps

As we discussed above, DevOps is inherently a cultural phenomenon. Therefore, by nature, its implementation is met with cultural resistance, and that’s where its challenges emerge. Here’s how it can be seen on practical ground:

• Integration complexity: From training the workforce in various DevOps tools to implementing concepts like automation, CI/CD, continuous monitoring, and more into the existing legacy infrastructure is a complex process with too many variables. The integration of DevOps gets even more challenging once the business starts to scale up.
• Security risks: The speed and automation benefits of DevOps make it difficult for security checkers to be embedded throughout the SDLC. There are false positives thrown by automated security tools that can further complicate things for security teams. 
• Integrating new technologies: Even with DevOps implemented, integrating technologies like AI, cloud, and more can be difficult. Though these technologies can help DevOps eliminate many of its challenges, they are too complicated and resource-intensive to be implemented in the first place.‍
• Architecture and scalability: Most software applications and platforms are run on microservices and containerized architectures. Managing these architectures can be difficult for DevOps in terms of version control, distributed tracing, security compliance, and more.

DevOps Best Practices

DevOps is founded on the belief that development and operations are not separate silos but part of a unified workflow. Therefore, the practices that truly align with this belief can be considered among the best. Here are some of the major ones:

• Infrastructure as Code (IaC): We now have tools like Terraform and Ansible that can help codify the infrastructure for DevOps. The environment is the fundamental need for DevOps, which makes IaC implementation necessary for a scalable and consistent environment. 
• Real-time performance tracking: The monitoring tools hired by DevOps teams must be capable of continuous observability for identifying and resolving issues in real time. With distributed systems and containerized architecture in place, this practice becomes more essential for modern DevOps needs.
• Shift-left Testing: Shift left, of course, means pulling the testing efforts as early in the SDLC as possible. This is a necessary practice in today’s DevOps strategies, as testing itself can slow down the development process for already complex applications and platforms. Shift-left can help DevOps maintain its promise of agility while ensuring continuous testing throughout the CI/CD pipeline.
• DevSecOps: It does not make sense to implement DevOps for any of today’s business needs without being cognizant of security. DevSecOps can help with this vigilance by offering automated vulnerability scanning, continuous code analysis, and other such security checks throughout the DevOps pipelines.
• Feedback loops: Continuous feedback is another among the core DevOps principles that ensure a fruitful collaboration of dev and ops teams. Automated monitoring tools can help operation teams log and flag deviations in customer experience and notify development teams of the same.

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DevOps Tools

Your DevOps toolchain must carry tools that can assist with CI/CD, container management, infrastructure codification, monitoring, and more. These tools support key DevOps practices, including project planning, code development, testing, deployment, and more. These tools bring automation, CI/CD management, a collaborative approach to software development, and uninterrupted monitoring and feedback.

With a motivation to ensure collaborative efforts, these tools can be used by development teams, system admins, QA teams, and security teams for their dedicated needs. With new technologies emerging, they are evolving for cloud-native environments, AI/ML integration, infrastructure as code (IaC) implementations, and security automation.

If you wish to learn more about these DevOps tools, check out our latest blog here.

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How DevOps can benefit from AI and ML?

The emergence of AI/ML has a disruptive impact on DevOps and the benefits it can offer. The technology enhances the core principles of DevOps in many ways. Moreover, we now also have AIOps and MLOps to further help the DevOps efforts. Let’s have a look at the benefits of integrating AI and ML into DevOps. 

1) Collaboration

For this principle, AI/ML helps DevOps ensure smooth communication between the collaborating teams, including dev, ops, and security. Here are the benefits AI/ML brings for collaboration in DevOps:

• Real-time communication: Facilitating real-time communications, including status updates, issue flags, performance reports, etc.
• Documentation: Automated documentation for consistent updates on system configurations, requirement additions, and codebase changes.
• DevOps support: Personalized assistance and chatbots to share suggestions among the teams regarding debugging, code deployment, and other aspects of the SDLC
• Continuous feedback: Feedback loops that help follow necessary performance metrics and offer feedback and analysis across teams.

2) Automation

• CI/CD Pipelines: DevOps automation is mostly sourced by its CI/CD capabilities. Integrating, deploying, and delivering the code updates might cause disruptions and interference when done by multiple teams. AI/ML-enabled DevOps can reduce the errors that can cause such disruptions.
• Automated Testing: Intelligent AI-based test cases further automate the testing process by helping prioritize test execution for critical parts of code and suggesting potential fixes and corrective actions.
• Security Automation: Automated security checks can be integrated to strengthen DevSecOps for threat detection, vulnerability flagging, and automated security patches.

3) Agility

• Predictive analytics: AI offers proactive problem-solving to ensure emphasis on agile frameworks. Such predictive intelligence is necessary for predicting potential system failures, performance issues, or bottlenecks.
• Testing: AI uplifts the power of shit-left to make test automation more agile. With AI-enabled testing, the sprints can be faster, and any functional bottlenecks can be addressed more quickly.

4) Consistency

• Continuous Monitoring: AI can help detect anomalies and security threats in real-time and offer proactive measures to deal with them.
• Automated rollbacks: Not only is CI/CD itself enhanced with AI, it is also a good ally in ensuring timely rollbacks in case of code failures. This helps reduce any downtimes. ‍
• Capacity Planning: AI helps optimize infrastructure and application performance by analyzing usage trends and forecasting resource needs. This will help DevOps teams ensure scalable systems without causing any disruption.