Top Best Practices for implementing DevOps

Kunal Mahajan 6th Nov 2023 - 5 mins read

DevOps best practices include agile project management, automation, CI/CD, monitoring, observability, and continuous feedback.

What is DevOps?

DevOps is the combination of cultural philosophies, practices, and tools that improves an organization's capacity to deliver applications and services at high velocity: evolving and improving products more quickly than organizations using conventional infrastructure management and software development procedures. Organizations may provide better customer service and compete more successfully in the market because of this speed.

What is the importance of DevOps?

DevOps implementation spans every component of an IT ecosystem and necessitates an organizational-wide culture transformation.

According to Puppet’s 2018 State of DevOps report, elite performers deploy code 46 times more often, have a 2555 faster lead time from code commit to deployment, have a 7 times lower change failure rate and are 2604 times faster to recover from incidents.

DevOps improves quality as well as speed to market, with exceptional teams having a change failure rate seven times lower than low-performing teams.


Top Best Practices for implementing DevOps

  • Agile project management

Agile project management is an iterative method to project delivery that spans the project's life cycle. Agile projects, as opposed to a basic linear Waterfall approach, are made up of a number of smaller cycles. Each of them is a mini project, with stages of design, development, testing, and deployment inside a pre-defined scope of work.

A potentially shippable product increment is delivered at the end of each cycle. As a result, new features are added to the product with each iteration, resulting in progressive project expansion. The risks of producing a possibly failing product are considerably reduced when features are evaluated earlier.

Agile sticks to following best practices:

  • Flexibility
  • Work breakdown
  • Value of teamwork
  • Iterative improvements
  • Cooperation with a client

Agile project management frameworks and methodologies

  • Scrum: Sprint planning, Daily standups, Regular review of team’s performance.
  • Kanban: Visualize work, Set limit on number of tasks, Optimize workflow and identify bottlenecks to improve efficiency.
  • Extreme Programming (XP): : Pair programming, Test-Driven Development, Continuous integration (CI).
  • Lean: Identify and eliminate waste in processes, such as unnecessary documentation or repetitive tasks, Focus on delivering the most valuable features to the customer first.

  • Automation

Automation in DevOps refers to the practice of automating various aspects of the software development and IT operations lifecycle. It is a core principle of DevOps that aims to streamline and accelerate the delivery of software by reducing manual tasks, improving efficiency, and ensuring consistency. Automation plays a critical role in achieving the goals of DevOps, such as faster and more reliable software delivery, improved collaboration between development and operations teams, and enhanced feedback loops.

Key areas where automation is commonly applied in DevOps include:

  • Build and Continuous Integration (CI):

    Automating the build process, including compiling code, packaging, and creating artifacts. Running automated tests as part of the CI pipeline. Automatically triggering builds when code changes are committed.

  • Deployment and Continuous Deployment (CD):

    Automating the deployment of applications and services to various environments (e.g., development, staging, production). Implementing blue-green deployments or canary releases to minimize downtime and risks. Automatically rolling back deployments in case of issues.

  • Infrastructure as Code (IaC):

    Using tools like Terraform, Ansible, or CloudFormation to define and provision infrastructure in a code-like manner. Automatically scaling infrastructure resources based on demand.

  • Configuration Management:

    Managing system and application configurations using automation tools. Ensuring that systems remain in a desired state and automatically remediating any configuration drift.

  • Monitoring and Alerting:

    Automating the setup of monitoring and alerting systems to track the health and performance of applications and infrastructure. Implementing self-healing mechanisms to automatically respond to certain issues.

  • Log Management and Analysis:

    Automating the collection, aggregation, and analysis of logs from various sources. Implementing automated alerting and reporting based on log data.

  • Testing and Quality Assurance:

    Automating various types of testing, such as unit, integration, and performance testing. Using automated code analysis tools to maintain code quality.

  • Security and Compliance:

    Implementing security scanning and vulnerability assessments in the CI/CD pipeline. Automatically applying security patches and updates.

  • Release Management:

    Automating the release process, including versioning, change tracking, and release notes generation. Ensuring that releases are consistent and traceable.

  • Collaboration and Communication:

    Integrating chat-bots and notification systems to automate communication between teams and stakeholders. Automating the generation and sharing of reports and status updates.

  • Embrace Infrastructure as Code (IaC):

Infrastructure as Code (IaC) is a fundamental concept in DevOps that involves managing and provisioning infrastructure using code and automation. With IaC, you treat infrastructure components such as servers, networks, and storage as code, which allows you to define, configure, and deploy infrastructure resources in a predictable and consistent manner. This approach offers several benefits in the context of DevOps:

    Consistency Version Control Reproducibility Scalability Automation Self-service

There are several tools and approaches for implementing IaC in DevOps, including:

  • Terraform: Terraform is a popular IaC tool that uses a domain-specific language to define infrastructure as code. It supports a wide range of cloud providers and other infrastructure platforms.
  • Ansible: Ansible is an automation tool that can be used for both configuration management and IaC. It uses simple YAML files to define infrastructure and configuration tasks.
  • AWS CloudFormation: If you are primarily working with Amazon Web Services (AWS), CloudFormation provides native IaC capabilities for defining and provisioning AWS resources.
  • Azure Resource Manager Templates: For Microsoft Azure, ARM templates can be used to define infrastructure and services in a declarative manner.
  • Google Cloud Deployment Manager: Google Cloud's Deployment Manager is an IaC tool for defining and deploying Google Cloud resources.

  • Continuous Integration and Continuous Deployment

DevOps and any modern software development practice would be incomplete without CI/CD. Through built-in automation, testing, and collaboration, a purpose-built CI/CD platform can maximize development time by raising an organization's productivity, increasing efficiency, and optimizing workflows.

Importance and benefits of CI/CD

  • Speed to market
  • Quality assurance
  • Risk Reduction
  • Enhanced collaboration
  • Improved code quality

CI/CD Best practices

  • Version control
  • Automated testing
  • Deployment automation
  • Infrastructure as Code
  • Continuous Monitoring

CI/CD Tools

  • Jenkins
  • AWS
  • Gitlab CI/CD

  • Monitoring and logging the DevOps pipeline and applications

Monitoring and logging are the unsung heroes of DevOps. They enable you to ensure the reliability and performance of your applications, respond to issues promptly, and continuously improve your software. By adopting best practices and employing the right tools, you can empower your DevOps teams to deliver top-notch software and services.

Significance of Monitoring and Logging

  • Real-time Insight

Monitoring and logging provide real-time insight into the health and performance of your applications. This is crucial for identifying issues promptly and ensuring that your applications meet their Service Level Objectives (SLOs).

  • Proactive Issue Detection

By monitoring and logging, you can proactively detect and resolve issues before they escalate, minimizing downtime and user disruption.

  • Root Cause Analysis

Logs and monitoring data help in root cause analysis, enabling you to identify why an issue occurred and how to prevent it in the future.

Monitoring practices

  • Infrastructure Monitoring

Monitor servers, networks, and databases for metrics like CPU usage, memory consumption, and network latency. Tools like Prometheus and Nagios are valuable for this.

  • Application Performance Monitoring (APM)

Use APM tools like New Relic or AppDynamics to track the performance of your applications, including response times, transaction traces, and error rates.

  • User Experience Monitoring

Employ user experience monitoring tools to capture how real users interact with your application. This provides insights into user behavior and any issues they might face.

Logging practices

  • Centralized Logging

Implement a centralized logging system, where logs from various components and microservices are aggregated and stored in a central repository. Elasticsearch and Logstash (ELK) stack are popular choices for this.

  • Structured Logging

Use structured logging to make log data more readable and filterable. Log entries should include relevant metadata, such as timestamps, severity levels, and source components.

  • Error Logging

Pay special attention to error logging. Log all application errors, exceptions, and failures. This data is essential for troubleshooting and improvement.


  • Grafana
  • Prometheus
  • ELK Stack (Elasticsearch, Logstash, Kibana)

Best practices

  • Automated alerts
  • Log retention policies

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