DevOps at Sfinitor

Rather than uniformly implementing a comprehensive monitoring and logging system over all systems, we typically opt for tailored monitoring and logging for essential application and infrastructure elements as well as user experience factors. By doing so, we minimize excessive data (noise) and concentrate resources on aspects with the greatest influence on business functionality.

Employing performance benchmarks and anomaly detection methodologies, we design alerts with a low false positive rate. This ensures that solely significant problems prompt notifications.

We employ automated response scripts for frequent incidents, noticeably decreasing the Average Time to Resolution (ATR). For instance, instantly adjusting resources in face of traffic surges ensures system stability and optimal performance without human intervention. Yet, it's understood that not every process necessitates automation; human judgment is essential for determining root causes of performance bottlenecks or increased cloud expenditure.

By implementing ITIL methodologies, we streamline incident response by leveraging tools such as ServiceNow for structured processes. These incorporate defined on-call rotations and escalation protocols, ensuring swift resolution and reducing downtime impact. To maintain continuous improvement, feedback loops from operations to development are established. This enables monitoring insights to inform future software updates, enhancing quality and aligning developments with operational realities.

1. Designing robust software architecture

Employing modular app structures optimizes automation in testing, deployment, monitoring, and scaling. Preferred choices include microservices, event-driven designs, service meshes, and serverless systems, which streamline operations for enhanced efficiency.

2. Tech Stack Selection

Preferring a multi-tool approach over singular platforms for their inherent restrictions, we strategically utilize top-tier DevOps tools. In constructing tailored DevOps toolchains, we opt for software that complements the distinctive technology stack of each project, seamlessly integrates with pre-existing systems and other DevOps elements, is adaptable to scalability demands, and promotes effortless adoption among all project teams.

3. Establishing dev-test-prod infrastructure for seamless application deployment

By harnessing automation tools such as Ansible and Terraform, we eliminate the need for manual configuration of servers, networks, and associated infrastructure elements. This method, termed Infrastructure as Code (IaC), provides a uniform approach to managing infrastructure resources and configurations. The advantages are twofold: firstly, it guarantees consistency; secondly, it streamlines management through automated deployment and configuration of project infrastructures. Infrastructure code is stored within version control systems, ensuring modularity and facilitating flexible automation.

4. Software coding

Incorporating clean code streamlines automation within development workflows. At Sfinitor, we prioritize exceptional code quality through peer reviews, static analysis, compliance with coding standards, and thorough unit testing. Our teams utilize dependency management tools like npm (JavaScript), Maven (Java), and NuGet (.NET) to maintain stable build environments, reducing compatibility issues among components.

5. Software integration

Automate CI pipelines for seamless deployment through continuous integration.

• Rapidly detect defects via targeted unit testing
• Merge code modifications from various programmers into a unified repository
• Frequent merge of changes aids in promptly identifying integration problems
• Build app from provided source code
• Reproducibility and consistency of builds crucial in diverse settings
• Perform static code analysis to detect potential problems like security loopholes, poor coding practices, and stylistic discrepancies in the codebase
• Build and package artifacts (binaries, libraries) for downstream pipeline stages

6. Software testing

Prioritizing automation for repetitive tasks such as smoke and regression testing, along with high-volume requirements like load and performance testing, enables swift identification and resolution of issues. In a legacy application project encompassing vast scales, we utilized automated end-to-end UI smoke tests, achieving 56% coverage in mere 7 minutes. This significantly reduced the time spent on manual testing from 12 hours.

Automated testing is seamlessly incorporated within CI/CD pipelines, ensuring functionality validation post-code alterations.

In containerization and virtualization, we establish consistent, repeatable test settings for both automation and manual testing. These techniques minimize inconsistencies, thereby boosting the trustworthiness of tests.

7. Software deployment

In suitable scenarios, we establish continuous delivery and deployment (CD) pipelines for streamlining build deployment across staging, testing, and production environments. By leveraging end-to-end CI/CD workflows, Sfinitor's development teams can seamlessly integrate, test, and deploy new functionalities in just 2-3 hours.

Embracing Infrastructure as Code (IaC), our team utilizes version control systems such as Git, Team Foundation Server (TFS), and Apache Subversion to monitor and manage code modifications, fostering seamless cross-functional collaboration. The applications we develop are containerized using Docker for consistent runtime environments, while Kubernetes automates scaling and administration tasks for streamlined operations. To orchestrate multi-environment deployments, we capitalize on tools like Octopus Deploy and Azure Release Pipelines.