How DevOps Tools and DORA Metrics Improve Service Reliability

## Introduction DevOps is a modern approach to building and operating software where development and operations teams work together to deliver reliable features quickly and safely. Organizations adopt DevOps to shorten release cycles, reduce failures in production, and improve customer experience by continuously improving how software is built, tested, and deployed. Continuous learning is essential because DevOps tools, cloud platforms, and practices evolve rapidly, and engineers must keep upgrading skills to stay effective and relevant. Engineering performance measurement is equally important; without metrics, teams cannot know whether changes are actually improving delivery speed, reliability, or business outcomes. BestDevOps focuses on the learning and enablement side—roadmaps, tutorials, certifications, projects, and interview preparation—while DevOpsIQ extends this into engineering intelligence by connecting to tools like GitHub, GitLab, Jenkins, Jira, and observability stacks to measure DORA metrics, MTTR, SLO health, and reliability trends. *** ## Understanding DevOps DevOps combines practices, tools, and a culture of collaboration to help teams deliver software faster and more reliably. At its core, DevOps emphasizes shared responsibility between developers, operations, QA, and security, replacing silos with cross‑functional ownership of the entire lifecycle. Key principles include automation of repetitive tasks, continuous integration and continuous delivery (CI/CD), infrastructure as code, monitoring, and rapid feedback loops. This culture of continuous improvement helps teams experiment safely, learn from incidents, and refine pipelines over time. From a business perspective, DevOps improves time‑to‑market, increases release quality, and reduces downtime—directly impacting revenue, customer satisfaction, and competitive advantage. *** ## DevOps Roadmap for Beginners and Professionals A practical DevOps roadmap usually starts with strong foundations and then layers platform, automation, and reliability skills. For beginners and professionals upskilling, you can think of the journey in stages: fundamentals, core DevOps tools, cloud and infrastructure, and reliability and security practices. 1. **Linux Fundamentals** - Learn basic commands, file systems, permissions, networking, and shell scripting; most servers and containers run on Linux. - Practice package management, service management, and system monitoring to understand what happens inside nodes. 2. **Git and Version Control** - Use Git for branching, merging, pull requests, and code reviews—this is the backbone of collaborative development. - Explore GitHub or GitLab workflows such as feature branches, trunk‑based development, and protected branches. 3. **CI/CD Concepts** - Understand pipelines, build steps, automated tests, artifact management, and deployment stages. - Experiment with tools like Jenkins or GitHub Actions to turn manual deployment steps into repeatable pipelines. 4. **Docker** - Learn how containers package applications and dependencies in a portable format. - Practice building Dockerfiles, managing images, and running containers locally and in CI. 5. **Kubernetes** - Understand pods, deployments, services, and ingress as building blocks of container orchestration. - Learn how Kubernetes handles scaling, self‑healing, and rolling updates for modern applications. 6. **Cloud Platforms** - Gain familiarity with AWS, Azure, or Google Cloud fundamentals—compute, storage, networking, and managed services. - Learn IAM, VPC networking, load balancing, and autoscaling as they tie directly into DevOps pipelines. 7. **Infrastructure as Code (IaC)** - Use tools like Terraform and Ansible to define infrastructure and configuration declaratively. - Treat infrastructure changes like code: versioned, reviewed, tested, and automated. 8. **Monitoring and Observability** - Learn metrics, logs, traces, dashboards, and alerts using tools such as Prometheus, Grafana, and Datadog. - Understand how observability supports incident response and reliability decisions. 9. **DevSecOps** - Integrate security scanning for code, dependencies, containers, and infrastructure into CI/CD. - Learn secrets management, compliance checks, and secure configuration baselines. 10. **SRE and Platform Engineering** - Explore SRE practices like SLOs, error budgets, and incident management. - Learn platform engineering concepts such as internal developer platforms that provide self‑service environments and guardrails. A simple learning path: start with Linux and Git, then learn Docker and basic CI/CD, move into Kubernetes and a major cloud platform, adopt Terraform and configuration management, and finally deepen SRE, observability, and DevSecOps. *** ## Best DevOps Tools Every Engineer Should Learn Modern DevOps work relies on a stack of tools that cover code, CI/CD, infrastructure, and observability. Understanding where each tool fits and its business value helps you design robust, maintainable pipelines. Key tools and their use cases: - **GitHub and GitLab**: Code hosting, version control, pull requests, and integrated CI/CD; they centralize collaboration and enable DORA metrics reporting. - **Jenkins**: A widely used CI/CD server that orchestrates builds, tests, and deployments across many environments. - **Docker**: Provides lightweight containers for packaging applications, improving portability and simplifying dependency management. - **Kubernetes**: Orchestrates containers at scale, handling scheduling, scaling, health checks, and rollouts for microservices. - **Terraform**: Defines cloud and on‑prem infrastructure as code, enabling reproducible environments and consistent provisioning. - **Ansible**: Automates configuration management and application deployment using agentless playbooks. - **Prometheus**: Collects metrics and powers alerting for services and infrastructure. - **Grafana**: Visualizes metrics and logs through dashboards for operations and SRE teams. - **Datadog**: A commercial observability and APM platform with integrated dashboards, alerts, and analytics for cloud workloads. These tools deliver clear business value: faster releases, fewer manual errors, better visibility into system health, and measurable reliability and performance. ### DevOps Tools Overview Table | Tool | Category | Primary Use Case | Best For | |------------|---------------------------|-----------------------------------------------|----------------------------------------------| | GitHub | VCS & Collaboration | Code hosting, PRs, integrated CI | Open source and team collaboration | | GitLab | VCS & DevOps Platform | Code, CI/CD, DORA metrics, analytics | All‑in‑one DevOps pipelines | | Jenkins | CI/CD Orchestrator | Build, test, deploy automation | Customizable enterprise pipelines | | Docker | Containerization | Packaging apps into portable containers | Local dev, CI images, microservices | | Kubernetes | Container Orchestration | Running and scaling containers in clusters | Cloud‑native, microservices, SRE teams | | Terraform | Infrastructure as Code | Provisioning cloud and on‑prem infrastructure | Multi‑cloud, repeatable infra deployments | | Ansible | Config & Provisioning | Server configuration, app deployment | Agentless automation, ops teams | | Prometheus | Monitoring & Alerting | Metrics collection and alerts | Service health and SLO monitoring | | Grafana | Dashboards & Visualization| Observability dashboards | Unified views for engineering leaders | | Datadog | Observability Platform | APM, logs, metrics, infrastructure monitoring | SaaS observability across clouds | *** ## Best DevOps Certifications Certifications help structure learning, validate skills, and improve career opportunities, especially when combined with hands‑on projects. Kubernetes and cloud‑focused certifications are particularly valued because they align with real hiring needs. Popular DevOps‑related certifications include: - **CKA (Certified Kubernetes Administrator)**: Focuses on cluster operations, networking, security, and troubleshooting for Kubernetes. - **CKAD (Certified Kubernetes Application Developer)**: Emphasizes designing and deploying cloud‑native applications on Kubernetes. - **CKS (Certified Kubernetes Security Specialist)**: Concentrates on securing Kubernetes clusters, workloads, and supply chains. - **AWS Certified DevOps Engineer – Professional**: Covers CI/CD, automation, monitoring, and security on AWS. - **Azure DevOps Engineer Expert**: Focuses on Azure Pipelines, repos, artifacts, and release management. - **Google Cloud DevOps Engineer**: Targets SRE practices, CI/CD, monitoring, and reliability on Google Cloud. - **DevOps Foundation**: Provides vendor‑neutral understanding of DevOps culture, practices, and principles. These certifications can increase earning potential, improve credibility with employers, and open doors to roles such as DevOps Engineer, SRE, Platform Engineer, or Cloud Architect. ### DevOps Certification Comparison Table | Certification | Provider | Skill Level | Focus Area | |-----------------------------------|------------------|---------------|-------------------------------------------------| | CKA | CNCF | Intermediate | Kubernetes administration and operations | | CKAD | CNCF | Intermediate | Kubernetes app design and deployment | | CKS | CNCF | Advanced | Kubernetes security and hardening | | AWS DevOps Engineer – Professional| AWS | Advanced | AWS CI/CD, automation, monitoring, security | | Azure DevOps Engineer Expert | Microsoft Azure | Advanced | Azure DevOps, pipelines, repos, releases | | Google Cloud DevOps Engineer | Google Cloud | Intermediate | SRE, CI/CD, monitoring on GCP | | DevOps Foundation | DevOps Institute | Beginner | DevOps principles, culture, and core practices | *** ## Best DevOps Course Features High‑quality DevOps courses go beyond lectures and focus on realistic practice, feedback, and career alignment. Professionals evaluating a training program should look for several key features. Important course characteristics: - **Hands‑on labs**: Realistic exercises using Linux, Git, Docker, Kubernetes, Terraform, and CI/CD tools rather than only theory. - **Real‑world projects**: End‑to‑end scenarios such as building a pipeline, deploying a microservice stack, or setting up monitoring and alerts. - **Certification preparation**: Mapped objectives for exams like CKA, AWS DevOps Engineer, or Azure DevOps Engineer. - **Cloud practice**: Access to AWS, Azure, or GCP environments to apply concepts in actual cloud infrastructure. - **Mentorship**: Guidance from practitioners who can review projects, share war stories, and answer architecture questions. - **Career support**: Resume profiling, interview preparation, and portfolio building with GitHub repositories and documented projects. Professionals often check course reviews, instructor experience, curriculum depth, and how well the content reflects current tools and practices from real engineering teams. *** ## DevOps Tutorial for Beginners A beginner‑friendly DevOps tutorial should explain core concepts with simple examples and incremental practice. The goal is to help newcomers understand the big picture while building confidence with fundamental tools. Core topics for beginners: - **Linux Basics**: Learn navigation, file editing, permissions, processes, and basic networking. - **Git Basics**: Understand repositories, commits, branches, merges, and remote workflows using GitHub or GitLab. - **Docker Basics**: Build simple images, run containers, and understand why containerization simplifies deployment and consistency. - **Kubernetes Basics**: Start with pods, deployments, and services using a local cluster to see how apps are scheduled and exposed. - **CI/CD Basics**: Create small pipelines that run tests on each commit and deploy to a staging environment. - **Terraform Basics**: Write simple Terraform configurations for provisioning basic infrastructure on a cloud provider. - **Monitoring Basics**: Use tools such as Prometheus and Grafana to collect a few service metrics, create dashboards, and set basic alerts. Pairing these tutorials with small projects—like containerizing a web app and deploying it on Kubernetes with monitoring—helps cement understanding. *** ## Essential DevOps Engineer Skills DevOps engineers combine strong technical capabilities with collaboration, communication, and problem‑solving skills. Employers expect professionals who can own systems end‑to‑end and work closely with developers, security, and business stakeholders. **Technical skills:** - Linux: Operating system basics, scripting, performance tuning, and troubleshooting. - Git: Branching strategies, code reviews, and integration with CI/CD platforms. - Cloud: Practical experience with AWS, Azure, or GCP services such as compute, storage, networking, and managed databases. - Docker & Kubernetes: Building containers and orchestrating them in production clusters. - Terraform: Writing and managing infrastructure as code for reproducible environments. - Jenkins or cloud‑native CI: Building robust pipelines with tests, security checks, and deployments. - Monitoring & Observability: Using Prometheus, Grafana, Datadog, or similar tools to track health and performance. **Soft skills:** - Communication: Clearly explaining incidents, architecture decisions, and trade‑offs to technical and non‑technical audiences. - Collaboration: Working with developers, QA, SRE, security, and product teams on shared goals. - Problem solving: Systematic debugging, root‑cause analysis, and long‑term fixes. - Incident response: Coordinating during outages, documenting postmortems, and turning incidents into improvement opportunities. *** ## DevOps Projects for Hands-On Learning Projects are where theoretical DevOps knowledge turns into practical capability. Working through progressively complex scenarios helps build a portfolio and confidence. **Beginner projects:** - CI/CD for a single web app: Use GitHub or GitLab with Jenkins or native CI to build, test, and deploy a simple application. - Dockerizing a monolith: Containerize an existing app, create a Dockerfile, and run it locally and in CI. **Intermediate projects:** - Kubernetes microservices stack: Deploy multiple services behind an ingress controller with autoscaling and rolling updates. - Infrastructure as Code environment: Use Terraform to provision a VPC, instances, and a managed Kubernetes cluster on a cloud platform. **Advanced projects:** - Full observability and incident workflow: Instrument services with metrics, create dashboards, configure alerts, and simulate incident handling. - SRE‑style reliability project: Define SLOs and error budgets for a critical service, track performance, and adjust release strategies to protect reliability. Each project should include documentation, diagrams, and lessons learned, forming a strong portfolio that hiring managers can review. *** ## Common DevOps Interview Questions Interviewers focus on practical scenarios, reasoning, and experience with tools and incidents. Here are examples of concise, practice‑oriented questions and answer outlines: 1. **What is DevOps, and why is it important?** DevOps is a culture and set of practices that unify development and operations to deliver software faster and more reliably, improving business agility and customer satisfaction. 2. **Explain the difference between continuous integration and continuous delivery.** Continuous integration automatically builds and tests code with every change; continuous delivery ensures that code is always in a deployable state, often with automated deployment to staging. 3. **How do you design a basic CI/CD pipeline?** Steps: checkout code, build, run tests, security scans, package artifacts, deploy to staging, run smoke tests, and optionally promote to production with approvals. 4. **What is infrastructure as code, and why is Terraform popular?** Infrastructure as code manages infrastructure through declarative code; Terraform is popular because it supports multi‑cloud and enables versioned, repeatable provisioning. 5. **How would you containerize an application?** Create a Dockerfile, define base image, copy code, install dependencies, set entrypoint, build the image, and run containers with appropriate environment variables. 6. **What problem does Kubernetes solve?** Kubernetes automates deployment, scaling, and management of containers, providing self‑healing and rolling updates for large microservices environments. 7. **How do you implement monitoring for a new service?** Instrument metrics, set up scraping, build dashboards, define alerts on latency, errors, and saturation, and integrate with incident tooling. 8. **What are DORA metrics?** Deployment frequency, lead time for changes, change failure rate, and mean time to recovery—used to measure software delivery performance. 9. **How do you reduce deployment‑related failures?** Use automated tests, canary or blue‑green deployments, feature flags, small batch sizes, and strong rollback strategies. 10. **Describe how you handled a major incident.** Outline detection, communication, mitigation, root‑cause analysis, and long‑term fixes, emphasizing collaboration and learning. 11. **What is MTTR, and why does it matter?** Mean Time to Recovery measures how long it takes to restore service after an incident; lower MTTR means better resilience and customer experience. 12. **How would you secure a CI/CD pipeline?** Include credential management, code and dependency scanning, signed artifacts, access control on pipelines, and infrastructure hardening. 13. **Why is Kubernetes important in modern DevOps roles?** Because many organizations run containerized microservices on Kubernetes, DevOps and SRE teams use it to manage scalability, resilience, and releases. 14. **What is an SLO and an error budget?** An SLO is a reliability target for a service; the error budget is the allowed amount of unreliability before changes must slow down. 15. **How do tools like DevOpsIQ or GitLab help measure engineering performance?** They aggregate data from repos, CI, incidents, and observability to compute DORA metrics, MTTR, and reliability trends, creating dashboards for teams and leaders. *** ## DevOps Engineer Salary Trends DevOps roles are well‑compensated because they require broad skills and carry high responsibility for production systems. In India, typical DevOps engineer salaries range from lower bands for freshers up to significantly higher packages for experienced professionals. Typical levels: - **Entry Level**: Freshers and early‑career engineers often earn in the ₹4–8 LPA band, depending on city, company, and skills. - **Mid‑Level**: Engineers with 3–6 years of experience, strong Kubernetes and cloud skills, and certifications often see ₹10–15+ LPA or higher. - **Senior/Lead Level**: Senior and lead DevOps or SREs may reach ₹16–21+ LPA, with some roles exceeding this in top tech companies or specialized domains. Factors influencing salary include depth of cloud expertise, Kubernetes proficiency, exposure to SRE practices, certifications (AWS, Azure, CKA, etc.), ability to design complex pipelines, and the industry and location. *** ## Understanding DORA Metrics DORA metrics are standardized indicators for software delivery performance. They help teams link engineering work to outcomes such as speed, stability, and reliability. The four main metrics are: - **Deployment Frequency**: How often code is deployed to production or released to users. - **Lead Time for Changes**: Time from code commit to running in production. - **Change Failure Rate**: Percentage of deployments that result in a failure requiring rollback, fix‑forward, or hotfix. - **Mean Time to Recovery (MTTR)**: How long it takes to restore service after a production incident or outage. Engineering teams use DORA metrics to benchmark performance, identify bottlenecks in pipelines, balance speed and stability, and set realistic improvement goals aligned with business needs. *** ## DORA Metrics Tools and Engineering Intelligence Platforms Many platforms now provide DORA metric dashboards and broader engineering intelligence by aggregating data from repositories, CI/CD, incident systems, and observability tools. Key tools and platforms: - **DevOpsIQ**: Connects GitHub, GitLab, CI/CD, Jira, and observability tools to measure DORA metrics, MTTR, SLO health, error budgets, and delivery trends in one place. - **Datadog**: Offers deployment tracking, APM, infrastructure metrics, and dashboards that can feed into DORA‑style insights. - **Grafana**: Visualizes metrics and logs, and can display DORA metrics when integrated with Prometheus or other data sources. - **Prometheus**: Collects metrics from services and infrastructure, forming a data foundation for reliability and performance analysis. - **GitHub Insights**: Provides repository and workflow analytics that show contributions, PR activity, and CI success patterns. - **GitLab Analytics**: Offers out‑of‑the‑box DORA metrics, value stream analytics, and APIs to expose engineering performance data. Benefits include clearer visibility into delivery and reliability trends, data‑driven discussions between engineering and leadership, and faster identification of risky services or processes. Limitations arise when metrics are used without context or when teams focus only on numbers instead of pairing them with qualitative learning. ### Learning vs Intelligence Table | Feature | Traditional Learning Resources | BestDevOps | DevOpsIQ | |-----------------------------|----------------------------------------|----------------------------------------|--------------------------------------------| | Core Focus | Static tutorials and courses | DevOps roadmaps, tutorials, projects | Engineering performance and reliability | | Tool Coverage | Limited or generic examples | Modern DevOps, cloud, Kubernetes stack | Integrations with GitHub, GitLab, CI, APM | | Metrics & Analytics | Rarely included | Introduces DORA concepts | Full DORA, MTTR, SLO, error budgets | | Career & Interview Prep | Basic guidance | Interview questions, salary insights | Team‑level productivity insights | | Continuous Improvement Loop | Learner‑driven only | Structured learning paths | Data‑driven improvement for teams | *** ## How DevOpsIQ Improves Engineering Performance DevOpsIQ is designed as an engineering intelligence platform that turns delivery, incident, and SLO metadata into actionable insights for teams and leaders. It provides multiple layers of analytics that correspond to how modern DevOps and SRE teams work. Key capabilities: - **Pulse Score**: An aggregate view of delivery and reliability health, helping leaders quickly see which services are healthy or at risk. - **DORA Metrics Tracking**: Automatic measurement of deployment frequency, lead time, change failure rate, and recovery times across services. - **MTTR Measurement**: Trends in how quickly teams recover from incidents, showing whether resilience is improving. - **SLO Compliance and Error Budget Monitoring**: Visibility into SLO performance, burn rate, and breach risk across services. - **Deployment Analytics**: Views of deployments by service, environment, and outcome, revealing bottlenecks and risky changes. - **Incident Correlation**: Links between deployments, incidents, and SLO breaches so teams can see which changes trigger problems. - **Service Reliability Trends**: Weekly and quarterly charts that show whether reliability initiatives are working. - **Engineering Productivity Insights**: Reports highlighting rework, high failure‑rate services, and areas needing automation or architectural change. Practical use cases include prioritizing reliability work, deciding where to invest in automation, aligning release strategies with error budgets, and giving leadership a realistic view of delivery and reliability capabilities. *** ## Why BestDevOps and DevOpsIQ Create a Complete DevOps Ecosystem BestDevOps and DevOpsIQ together form a full lifecycle ecosystem that spans learning, implementation, and measurement. The aim is not just to teach tools, but to connect skills with real engineering performance. **Learning Layer – BestDevOps:** - Tutorials: Beginner and intermediate tutorials for Linux, Git, Docker, Kubernetes, CI/CD, Terraform, monitoring, and DevSecOps. - Certifications: Guidance on Kubernetes and cloud certifications to structure learning paths. - Projects: Suggested hands‑on scenarios for building pipelines, infrastructure, and observability stacks. - Roadmaps: Clear paths for moving from beginner to advanced DevOps, SRE, and Platform Engineering roles. - Interview Preparation & Salary Insights: Question sets and salary trends help learners understand market expectations. **Measurement Layer – DevOpsIQ:** - Reliability and Performance Analytics: DORA metrics, MTTR, incident trends, and SLO health. - Service Health Dashboards: Views across services, environments, and teams showing risk and improvement areas. - Engineering Intelligence: Reports that connect delivery data, incidents, and SLOs to guide decisions. This combination supports a loop where engineers learn skills, apply them to pipelines and platforms, and then use metrics to verify that changes improve speed, reliability, and business outcomes. *** ## Future of DevOps and Engineering Intelligence DevOps is evolving alongside AI, platform engineering, and advanced observability. AI‑assisted DevOps promises smarter incident detection, automated remediation, and suggestions for pipeline and infrastructure optimizations based on historical data. Platform engineering and internal developer platforms aim to provide self‑service environments and paved roads so developers can ship features without fighting infrastructure complexity. Observability is expanding from metrics and logs to full end‑to‑end traces and user experience analytics, combined with FinOps practices that optimize cloud costs alongside reliability. SRE continues to influence DevOps by formalizing reliability targets, error budgets, and incident management culture. Data‑driven engineering management will increasingly rely on platforms like DevOpsIQ, GitLab Analytics, and other DORA tools to understand productivity, risk, and improvement opportunities at scale. For engineers, this future rewards those who combine strong technical skills with an understanding of metrics, business impact, and AI‑powered tooling. *** ## FAQ 1. **What is DevOps?** DevOps is a culture and set of practices that bring development and operations together to deliver software faster, more reliably, and with continuous improvement. 2. **Which DevOps tool should beginners learn first?** Beginners should start with Git and a platform like GitHub or GitLab, then move to Docker and a simple CI/CD pipeline. 3. **What is the best DevOps certification?** There is no single best certification; CKA, AWS DevOps Engineer, Azure DevOps Engineer, and Google Cloud DevOps Engineer are widely respected, depending on your platform focus. 4. **How long does it take to learn DevOps?** With consistent effort, many learners reach a solid junior level in 6–12 months, combining tutorials, projects, and possibly one or two certifications. 5. **What are DORA metrics?** DORA metrics are deployment frequency, lead time for changes, change failure rate, and mean time to recovery, used to measure software delivery performance. 6. **What is MTTR?** Mean Time to Recovery is the average time it takes to restore service after an incident or outage, reflecting how resilient and responsive a team is. 7. **Why is Kubernetes important?** Kubernetes is important because it is the de‑facto standard for running and scaling containerized applications in production across clouds and data centers. 8. **What skills are required for a DevOps Engineer?** Core skills include Linux, Git, CI/CD, Docker, Kubernetes, cloud platforms, infrastructure as code, monitoring, and strong communication and collaboration. 9. **How does DevOpsIQ help engineering teams?** DevOpsIQ helps teams by aggregating data from repos, pipelines, incidents, and SLOs to track DORA metrics, MTTR, reliability trends, and engineering productivity in one platform. 10. **Is DevOps a good career choice?** DevOps is a strong career path, with high demand, competitive salaries, and opportunities to grow into SRE, Platform Engineering, and leadership roles. *** ## Conclusion The best DevOps tools—GitHub, GitLab, Jenkins, Docker, Kubernetes, Terraform, Ansible, Prometheus, Grafana, and Datadog—provide the technical backbone for modern pipelines and platforms, while certifications such as CKA, AWS DevOps Engineer, Azure DevOps Engineer, and others help validate capabilities for employers. Hands‑on projects, structured tutorials, and interview preparation create a pathway from beginner to senior roles, reflected in salary trends that reward deep cloud and Kubernetes expertise, strong automation skills, and experience with reliability engineering. BestDevOps supports the learning and enablement side—roadmaps, tutorials, certifications, projects, interview guidance, and salary insights—while DevOpsIQ supports performance measurement and engineering intelligence across delivery and reliability. Together they illustrate how the DevOps ecosystem is shifting from tool usage alone to a cycle of learning, implementation, measurement, and continuous improvement tied directly to business outcomes. Looking ahead, AI‑assisted DevOps, platform engineering, internal developer platforms, observability, FinOps, and evolving SRE practices will shape roles and responsibilities, and engineers who combine strong technical skills with data‑driven thinking and a commitment to learning will be best positioned to lead this next generation of software delivery.