CloudSpikes MultiCloud Solutions Inc.

As an IT professional, version control with GIT has become an indispensable skill. However, grasping the core concepts of GIT can initially appear overwhelming. In this post, I'll provide an overview of essential GIT concepts and commands to help you get started. Key Concepts: 1. Repository: The central hub where your project files and commit history are stored. 2. Commit: A snapshot of changes, serving as a version checkpoint in your project's timeline. 3. Branch: A parallel timeline of commits, enabling you to work on different versions simultaneously. 4. Merge: The process of combining changes from separate branches into a unified version. 5. Pull Request: A mechanism to propose, review, and discuss changes before merging branches. Essential Commands: 1. 𝚐𝚒𝚝 𝚒𝚗𝚒𝚝: Initialize a new repository. 2. 𝚐𝚒𝚝 𝚜𝚝𝚊𝚝𝚞𝚜: View modified files that are not yet staged for commit. 3. 𝚐𝚒𝚝 𝚊𝚍𝚍: Stage files in preparation for committing. 4. 𝚐𝚒𝚝 𝚌𝚘𝚖𝚖𝚒𝚝: Create a commit with the staged snapshot of changes. 5. 𝚐𝚒𝚝 𝚋𝚛𝚊𝚗𝚌𝚑: List, create, or delete branches. 6. 𝚐𝚒𝚝 𝚌𝚑𝚎𝚌𝚔𝚘𝚞𝚝: Navigate between different branches. 7. 𝚐𝚒𝚝 𝚖𝚎𝚛𝚐𝚎: Combine the development histories of two branches. 8. 𝚐𝚒𝚝 𝚙𝚞𝚜𝚑/𝚙𝚞𝚕𝚕: Send or receive commits to/from a remote repository. By familiarizing yourself with these fundamental concepts and commands, you'll be well-equipped to leverage GIT for tracking changes, working in parallel branches, and collaborating effectively with others. While there is a vast array of additional GIT workflows, advanced commands, and integrations to explore, understanding these core elements will provide you with a solid foundation to embark on your GIT version control journey productively. Credit - Brij kishore Pandey 𝐅𝐨𝐥𝐥𝐨𝐰 𝐮𝐬 𝐨𝐧 𝐈𝐧𝐬𝐭𝐚𝐠𝐫𝐚𝐦 👉🏻https://lnkd.in/ehA5ePqX 𝐅𝐨𝐥𝐥𝐨𝐰 𝐮𝐬 𝐨𝐧 𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧 👉🏻 https://lnkd.in/e2sq98PN https://lnkd.in/e-9dJf8i 𝐅𝐨𝐥𝐥𝐨𝐰 𝐮𝐬 𝐨𝐧 𝐅𝐚𝐜𝐞𝐛𝐨𝐨𝐤 👉🏻 https://lnkd.in/eWcXVwAt

Do you want to use an integrated CI/CD service that is easy to set up and configure? 🙌 If you answered yes to any of these questions, then you need to check out Bitbucket Pipelines. 🚀 Bitbucket Pipelines is an integrated CI/CD service that allows you to automatically build, test, and deploy your code from your Bitbucket repository. 𝗬𝗼𝘂 𝗰𝗮𝗻 𝘂𝘀𝗲 𝗕𝗶𝘁𝗯𝘂𝗰𝗸𝗲𝘁 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 𝘁𝗼: 𝗕𝘂𝗶𝗹𝗱: You can compile your code and create artifacts using various tools and languages. You can also cache dependencies and speed up your build process. 𝗧𝗲𝘀𝘁: You can run tests on your code using various testing frameworks and tools. You can also generate test reports and coverage data. 𝗗𝗲𝗽𝗹𝗼𝘆: You can deploy your code and artifacts to various platforms and environments, such as AWS, Azure, Heroku, Firebase, etc. You can also use triggers and conditions to control when and how your deployment happens. 𝗔𝗻𝗱 𝗺𝗼𝗿𝗲: You can use Bitbucket Pipelines for any webhook event that happens on Bitbucket. You can also use Bitbucket Pipes for other tasks such as linting, formatting, security scanning, code analysis, etc. 🌟 But how do you get started with Bitbucket Pipelines? 🤔 Don’t worry, we’ve got you covered. 😉 This post is perfect for anyone who wants to learn the fundamentals of Bitbucket Pipelines. 𝐅𝐨𝐥𝐥𝐨𝐰 𝐮𝐬 𝐨𝐧 𝐈𝐧𝐬𝐭𝐚𝐠𝐫𝐚𝐦 👉🏻https://lnkd.in/ehA5ePqX 𝐅𝐨𝐥𝐥𝐨𝐰 𝐮𝐬 𝐨𝐧 𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧 👉🏻 https://lnkd.in/e2sq98PN https://lnkd.in/e-9dJf8i 𝐅𝐨𝐥𝐥𝐨𝐰 𝐮𝐬 𝐨𝐧 𝐅𝐚𝐜𝐞𝐛𝐨𝐨𝐤 👉🏻 https://lnkd.in/eWcXVwAt

𝐀𝐏𝐈 𝐆𝐚𝐭𝐞𝐰𝐚𝐲, 𝐋𝐨𝐚𝐝 𝐁𝐚𝐥𝐚𝐧𝐜𝐞𝐫, 𝐚𝐧𝐝 𝐑𝐞𝐯𝐞𝐫𝐬𝐞 𝐏𝐫𝐨𝐱𝐲: 𝐖𝐡𝐢𝐜𝐡 𝐎𝐧𝐞 𝐃𝐨 𝐘𝐨𝐮 𝐍𝐞𝐞𝐝? Traffic control in the digital world can feel like solving a complex puzzle. API Gateway, Load Balancer, Reverse Proxy—they all seem to do the same thing at first glance. But the truth? Their purposes are unique, and understanding them can transform your architecture. Let's unravel the mystery! 🔄 𝐑𝐞𝐯𝐞𝐫𝐬𝐞 𝐏𝐫𝐨𝐱𝐲: 𝐓𝐡𝐞 𝐆𝐚𝐭𝐞𝐤𝐞𝐞𝐩𝐞𝐫 A reverse proxy sits in front of your servers, handling client requests. Think of it as a middleman. 𝐊𝐞𝐲 𝐅𝐞𝐚𝐭𝐮𝐫𝐞𝐬: ✅ Improves security by hiding internal server details. ✅ Enables caching for faster responses. ✅ Simplifies SSL termination and encryption. When to Use It: You need to protect your backend infrastructure from direct exposure. You’re managing HTTP/HTTPS traffic effectively. 🎛️ 𝐋𝐨𝐚𝐝 𝐁𝐚𝐥𝐚𝐧𝐜𝐞𝐫: 𝐓𝐡𝐞 𝐓𝐫𝐚𝐟𝐟𝐢𝐜 𝐌𝐚𝐧𝐚𝐠𝐞𝐫 A load balancer ensures requests are spread across multiple servers to prevent overload. It's like directing traffic during rush hour. Key Features: ✅ Ensures high availability by distributing workloads. ✅ Provides failover to handle server outages. ✅ Supports scalability by managing increased traffic. When to Use It: You have multiple servers and need to maintain consistent performance. Uptime and reliability are your top priorities. 🔗 𝐀𝐏𝐈 𝐆𝐚𝐭𝐞𝐰𝐚𝐲: 𝐓𝐡𝐞 𝐀𝐏𝐈 𝐒𝐩𝐞𝐜𝐢𝐚𝐥𝐢𝐬𝐭 API Gateway acts as the single entry point for APIs, handling all the heavy lifting. It’s the concierge for your microservices. Key Features: ✅ Handles authentication, rate limiting, and logging. ✅ Simplifies API versioning and routing. ✅ Bridges communication between microservices. When to Use It: You’re managing multiple APIs and need centralized control. You want to offload cross-cutting concerns like security or monitoring. 🧩 𝐇𝐨𝐰 𝐭𝐨 𝐂𝐡𝐨𝐨𝐬𝐞? Use Reverse Proxy if you need enhanced security and basic traffic forwarding. Use Load Balancer to distribute traffic and ensure availability. Use API Gateway for a powerful, API-first architecture, especially with microservices. Credits to Satyender Sharma for this insightful creation. 𝐅𝐨𝐥𝐥𝐨𝐰 𝐮𝐬 𝐨𝐧 𝐈𝐧𝐬𝐭𝐚𝐠𝐫𝐚𝐦 👉🏻https://lnkd.in/ehA5ePqX 𝐅𝐨𝐥𝐥𝐨𝐰 𝐮𝐬 𝐨𝐧 𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧 👉🏻 https://lnkd.in/e2sq98PN https://lnkd.in/e-9dJf8i 𝐅𝐨𝐥𝐥𝐨𝐰 𝐮𝐬 𝐨𝐧 𝐅𝐚𝐜𝐞𝐛𝐨𝐨𝐤 👉🏻 https://lnkd.in/eWcXVwAt

Building and deploying microservices effectively requires more than just technical expertise—strategy and planning are key. Here’s a quick roadmap to guide you: - Database: Ensure each service has its own database to avoid bottlenecks and enable independent scaling. - Monitoring: Use tools like Prometheus and Grafana to monitor system health and performance. - Cloud Provider: Choose a cloud platform (AWS, Azure, GCP) that supports dynamic scaling and microservices management. - CI/CD: Automate testing and deployment pipelines for faster releases. - Message Broker: Use RabbitMQ or Kafka for reliable inter-service communication. - Languages: Use the right language for each service (Java, Python, Go). - Security: Implement strong security measures—TLS/SSL, OAuth2, and API gateways. - Container Orchestration: Use Kubernetes for scaling and managing containers. - Containers: Use Docker for consistent, isolated microservice environments. Focus on these essentials to ensure your microservices architecture is scalable, secure, and resilient. Credit: Ashish Joshi 𝐅𝐨𝐥𝐥𝐨𝐰 𝐮𝐬 𝐨𝐧 𝐈𝐧𝐬𝐭𝐚𝐠𝐫𝐚𝐦 👉🏻https://lnkd.in/ehA5ePqX 𝐅𝐨𝐥𝐥𝐨𝐰 𝐮𝐬 𝐨𝐧 𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧 👉🏻 https://lnkd.in/e2sq98PN https://lnkd.in/e-9dJf8i 𝐅𝐨𝐥𝐥𝐨𝐰 𝐮𝐬 𝐨𝐧 𝐅𝐚𝐜𝐞𝐛𝐨𝐨𝐤 👉🏻 https://lnkd.in/eWcXVwAt

🌟 Netflix Impressive Tech Stack! 🌟 Ever been curious about the tech wizardry that powers your favorite Netflix binge sessions? Here's a sneak peek into the technological marvel behind the scenes! 📱 Mobile & Web: Netflix goes native with Swift and Kotlin for mobile apps and relies on the versatile React framework for the web, ensuring a top-notch viewing experience. 🌐 Frontend/Server Communication: Netflix's secret sauce is GraphQL, making frontend-server communication smooth as silk. 🔨 Backend Services: The backbone of Netflix's reliability and scalability includes ZUUL, Eureka, and the Spring Boot framework. 💾 Databases: Netflix plays hard with EV cache, Cassandra, CockroachDB, and more, ensuring data accessibility and resilience. 📨 Messaging/Streaming: Real-time magic happens with Apache Kafka and Fink, delivering your content seamlessly. 🎥 Video Storage: Netflix stores and serves its vast library with S3 and Open Connect. 📊 Data Processing: Flink and Spark work their magic, followed by Tableau for visualization, while Redshift handles structured data warehousing. 🛠️ CI/CD: Netflix's CI/CD pipeline is finely tuned, thanks to tools like JIRA, Confluence, PagerDuty, Jenkins, Gradle, Chaos Monkey, Spinnaker, Altas, and more. 🌐 Netflix's unwavering commitment to tech excellence ensures a seamless, personalized, and secure streaming experience for millions worldwide. Hats off to the brilliant engineers and innovators driving this extraordinary architecture! Credit: Cloudairy 𝐅𝐨𝐥𝐥𝐨𝐰 𝐮𝐬 𝐨𝐧 𝐈𝐧𝐬𝐭𝐚𝐠𝐫𝐚𝐦 👉🏻https://lnkd.in/ehA5ePqX 𝐅𝐨𝐥𝐥𝐨𝐰 𝐮𝐬 𝐨𝐧 𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧 👉🏻 https://lnkd.in/e2sq98PN https://lnkd.in/e-9dJf8i 𝐅𝐨𝐥𝐥𝐨𝐰 𝐮𝐬 𝐨𝐧 𝐅𝐚𝐜𝐞𝐛𝐨𝐨𝐤 👉🏻 https://lnkd.in/eWcXVwAt

𝑻𝒆𝒓𝒓𝒂𝒇𝒐𝒓𝒎 𝑭𝒍𝒐𝒘 𝒊𝒏 𝑪𝑰/𝑪𝑫 𝑻𝒆𝒓𝒓𝒂𝒇𝒐𝒓𝒎 𝑫𝒆𝒗𝑶𝒑𝒔 1. 𝑫𝒆𝒗𝒆𝒍𝒐𝒑𝒆𝒓 - Role: The developer creates both the Terraform configuration files and the application code, ensuring that infrastructure and application requirements align seamlessly. 2. 𝑺𝒐𝒖𝒓𝒄𝒆 𝑪𝒐𝒏𝒕𝒓𝒐𝒍 - Process: After writing the code, the developer commits changes to a local Git repository. This is followed by pushing these commits to a remote repository, allowing for collaborative development and version control. 3. 𝑺𝒕𝒂𝒕𝒊𝒄 𝑪𝒐𝒅𝒆 𝑨𝒏𝒂𝒍𝒚𝒔𝒊𝒔 - Purpose: Before initiating the CI/CD pipeline, a static code analysis tool, such as SonarQube, scans the code for potential security vulnerabilities and assesses overall code quality. This step helps catch issues early in the development process. 4. 𝐂𝐈/𝐂𝐃 𝐓𝐨𝐨𝐥 𝐓𝐫𝐢𝐠𝐠𝐞𝐫 - Action: The push to the remote repository automatically triggers the CI/CD pipeline configured in Jenkins, initiating the automated workflow. 5. 𝐂𝐈/𝐂𝐃 𝐓𝐨𝐨𝐥𝐬 - Options: Various CI/CD tools are available, including CircleCI, GitHub Actions, and ArgoCD, providing flexibility based on project needs and team preferences. 6. 𝑻𝒆𝒓𝒓𝒂𝒇𝒐𝒓𝒎 𝑰𝒏𝒊𝒕𝒊𝒂𝒍𝒊𝒛𝒂𝒕𝒊𝒐𝒏 - Command: Jenkins executes the `terraform init` command to set up the Terraform working directory. This step involves downloading the necessary provider plugins to ensure proper configuration. 7. 𝑰𝒏𝒇𝒓𝒂𝒔𝒕𝒓𝒖𝒄𝒕𝒖𝒓𝒆 𝑷𝒍𝒂𝒏𝒏𝒊𝒏𝒈 - Execution: The `terraform plan` command is run by Jenkins, generating an execution plan that outlines the actions Terraform will take to achieve the desired state specified in the configuration files. 8. 𝑰𝒏𝒇𝒓𝒂𝒔𝒕𝒓𝒖𝒄𝒕𝒖𝒓𝒆 𝑨𝒑𝒑𝒍𝒊𝒄𝒂𝒕𝒊𝒐𝒏 - Implementation: Jenkins then runs `terraform apply`, applying the planned changes to the infrastructure. This step implements actual modifications to the cloud resources as defined in the Terraform configuration. 9. 𝑰𝒏𝒇𝒓𝒂𝒔𝒕𝒓𝒖𝒄𝒕𝒖𝒓𝒆 𝑫𝒆𝒑𝒍𝒐𝒚𝒎𝒆𝒏𝒕 - Outcome: The infrastructure is deployed to the designated cloud provider, such as AWS, Azure, or GCP, ensuring that resources are correctly provisioned. 10. 𝑰𝒏𝒇𝒓𝒂𝒔𝒕𝒓𝒖𝒄𝒕𝒖𝒓𝒆 𝑹𝒆𝒂𝒅𝒚 𝒇𝒐𝒓 𝑼𝒔𝒆 - Result: The deployed resources, including virtual machines, networks, and storage, are now provisioned and available for immediate use, enabling further development and deployment of applications. This structure improves readability while maintaining clarity, making it more engaging for your audience on social media or in presentations. Credits to Satyender Sharma for this insightful creation. 𝐅𝐨𝐥𝐥𝐨𝐰 𝐮𝐬 𝐨𝐧 𝐈𝐧𝐬𝐭𝐚𝐠𝐫𝐚𝐦 👉🏻https://lnkd.in/ehA5ePqX 𝐅𝐨𝐥𝐥𝐨𝐰 𝐮𝐬 𝐨𝐧 𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧 👉🏻 https://lnkd.in/e2sq98PN https://lnkd.in/e-9dJf8i 𝐅𝐨𝐥𝐥𝐨𝐰 𝐮𝐬 𝐨𝐧 𝐅𝐚𝐜𝐞𝐛𝐨𝐨𝐤 👉🏻 https://lnkd.in/eWcXVwAt

AWS management doesn't have to be a maze—these essential tools make it smooth and scalable! Mastering AWS Management Tools is essential for streamlined cloud operations! These tools play a vital role in managing, monitoring, and optimizing your AWS infrastructure. AWS Organizations, CloudFormation, Elastic Beanstalk, OpsWorks, CloudWatch, and CloudTrail are powerful components in this toolkit. Each tool brings unique capabilities to simplify resource management, automate deployments, ensure compliance, and provide real-time monitoring. Empower your cloud journey with AWS Management Tools to drive efficiency, maintain control, and enable seamless scalability in your cloud environment. Credit: Riyaz Sayyad 𝐅𝐨𝐥𝐥𝐨𝐰 𝐮𝐬 𝐨𝐧 𝐈𝐧𝐬𝐭𝐚𝐠𝐫𝐚𝐦 👉🏻https://lnkd.in/ehA5ePqX 𝐅𝐨𝐥𝐥𝐨𝐰 𝐮𝐬 𝐨𝐧 𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧 👉🏻 https://lnkd.in/e2sq98PN https://lnkd.in/e-9dJf8i 𝐅𝐨𝐥𝐥𝐨𝐰 𝐮𝐬 𝐨𝐧 𝐅𝐚𝐜𝐞𝐛𝐨𝐨𝐤 👉🏻 https://lnkd.in/eWcXVwAt

𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝘁𝗵𝗲 𝗞𝗮𝗳𝗸𝗮 𝗘𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺: Key Takeaways Kafka is the backbone for managing real-time data streams at scale. Here's a concise breakdown: 𝗣𝗿𝗼𝗱𝘂𝗰𝗲𝗿𝘀: Send data to specific topics in the Kafka cluster. 𝗖𝗼𝗻𝘀𝘂𝗺𝗲𝗿𝘀: Pull data from subscribed topics, often in groups for efficient parallel processing. 𝗧𝗼𝗽𝗶𝗰𝘀: Categories holding published data, further divided into 𝗽𝗮𝗿𝘁𝗶𝘁𝗶𝗼𝗻𝘀 for scalability. 𝗕𝗿𝗼𝗸𝗲𝗿𝘀: Individual Kafka servers storing partition data, working collectively in a 𝗰𝗹𝘂𝘀𝘁𝗲𝗿 to ensure fault tolerance and scalability. 𝗥𝗲𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻: Kafka’s Data Safety Net To prevent data loss during broker failures, Kafka replicates partitions. 𝗟𝗲𝗮𝗱𝗲𝗿 𝗥𝗲𝗽𝗹𝗶𝗰𝗮: Manages read/write requests. 𝗙𝗼𝗹𝗹𝗼𝘄𝗲𝗿 𝗥𝗲𝗽𝗹𝗶𝗰𝗮: Backup copies that can take over if the leader fails. Why It Matters Kafka’s architecture ensures scalability, reliability, and real-time performance, making it indispensable for modern data-driven systems. Credits to Satyender Sharma for this insightful creation. 𝐅𝐨𝐥𝐥𝐨𝐰 𝐮𝐬 𝐨𝐧 𝐈𝐧𝐬𝐭𝐚𝐠𝐫𝐚𝐦 👉🏻https://lnkd.in/ehA5ePqX 𝐅𝐨𝐥𝐥𝐨𝐰 𝐮𝐬 𝐨𝐧 𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧 👉🏻 https://lnkd.in/e2sq98PN https://lnkd.in/e-9dJf8i 𝐅𝐨𝐥𝐥𝐨𝐰 𝐮𝐬 𝐨𝐧 𝐅𝐚𝐜𝐞𝐛𝐨𝐨𝐤 👉🏻 https://lnkd.in/eWcXVwAt

𝐎𝐧𝐞 𝐩𝐨𝐬𝐭 𝐭𝐨 𝐫𝐞𝐟𝐞𝐫 𝐟𝐨𝐫 𝐃𝐞𝐯𝐨𝐩𝐬 𝐑𝐨𝐚𝐝𝐦𝐚𝐩 DevOps, short for Development and Operations, is a set of practices and cultural philosophies that aim to improve collaboration and communication between software development and IT operations teams. The primary goal of DevOps is to streamline the software delivery process, allowing organisations to deliver high-quality software more quickly and efficiently. DevOps emphasises automation, continuous integration, continuous delivery, and a feedback loop to enable faster and more reliable development and deployment of software. Here's a roadmap for DevOps: 1. Programming: Languages: Python, Bash, Ruby, Go, and/or JavaScript. Version Control: Git (GitHub, GitLab, Bitbucket). 2. Server Administration: Operating Systems: Linux (Ubuntu, CentOS, Debian). Configuration Management: Ansible, Puppet, or Chef. Containerization: Docker. 3. Network Security: Firewalls: iptables (Linux), pf (BSD), or cloudbased firewalls. VPN: OpenVPN, IPsec. Security Best Practices: Regular security audits, vulnerability scanning, and penetration testing. 4. Servers (Web, Database, Caching): Web Servers: Apache, Nginx. Databases: MySQL, PostgreSQL, MongoDB. Caching: Redis, Memcached. 5. Infrastructure as a Service (IaaS): Cloud Providers: AWS, Azure, Google Cloud Platform. Infrastructure Orchestration: Terraform. 6. Continuous Integration/Continuous Deployment (CI/CD): CI Tools: Jenkins, GitLab CI, Travis CI. CD Tools: Ansible, Kubernetes, Docker. 7. Monitoring and Logging: Monitoring Tools: Prometheus, Grafana, Nagios. Logging: ELK Stack (Elasticsearch, Logstash, Kibana), Splunk. 8. Clouds: Cloud Services: AWS (EC2, S3, RDS, Lambda), Azure, GCP. Serverless Computing: AWS Lambda, Azure Functions. Remember, practical experience and hands on projects are crucial for reinforcing these skills. As you progress, consider working on real-world projects, participating in open-source communities, and continuously staying updated with industry trends. Additionally, certifications in relevant areas can also boost your profile and provide structured learning paths. Did i miss any important areas , please let me know in comments ? Credit:- Rocky Bhatia 𝐅𝐨𝐥𝐥𝐨𝐰 𝐮𝐬 𝐨𝐧 𝐈𝐧𝐬𝐭𝐚𝐠𝐫𝐚𝐦 👉🏻https://lnkd.in/ehA5ePqX 𝐅𝐨𝐥𝐥𝐨𝐰 𝐮𝐬 𝐨𝐧 𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧 👉🏻 https://lnkd.in/e2sq98PN https://lnkd.in/e-9dJf8i 𝐅𝐨𝐥𝐥𝐨𝐰 𝐮𝐬 𝐨𝐧 𝐅𝐚𝐜𝐞𝐛𝐨𝐨𝐤 👉🏻 https://lnkd.in/eWcXVwAt

Why Metrics Matter By focusing on key metrics, you can enhance deployment speed, code quality, and system reliability, setting your team up for long-term success. Why are DevOps Metrics Important? - Measure Efficiency: They highlight how effectively development and operations processes are running. - Improve Quality: Tracking defect rates and deployment success helps enhance software reliability. - Enhance Decision-Making: Metrics provide data for informed decisions, prioritizing improvements and resource allocation. - Track Progress: They help monitor and achieve performance goals. Key DevOps Metrics - Deployment Frequency: Frequency of code deployments, indicating process maturity. - Lead Time for Changes: Time from code commit to deployment, reflecting agility. - Change Failure Rate: Percentage of failed deployments, showing code quality. - Mean Time to Recovery (MTTR): Average recovery time from failures, indicating process resilience. - Incident Frequency: Number of post-deployment incidents, assessing system stability. - Automated Test Coverage: Extent of test automation, leading to more reliable deployments. Getting Started with DevOps Metrics - Identify KPIs: Choose relevant metrics like deployment frequency or MTTR. - Set Up Monitoring Tools: Use platforms like Jenkins, Prometheus, or Splunk. - Analyze Data: Review data to find trends and areas for improvement. - Act on Insights: Make data-driven decisions to optimize processes. - Continuously Improve: Update metrics strategies to align with evolving goals. Credit: Ashish Joshi 𝐅𝐨𝐥𝐥𝐨𝐰 𝐮𝐬 𝐨𝐧 𝐈𝐧𝐬𝐭𝐚𝐠𝐫𝐚𝐦 👉🏻https://lnkd.in/ehA5ePqX 𝐅𝐨𝐥𝐥𝐨𝐰 𝐮𝐬 𝐨𝐧 𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧 👉🏻 https://lnkd.in/e2sq98PN https://lnkd.in/e-9dJf8i 𝐅𝐨𝐥𝐥𝐨𝐰 𝐮𝐬 𝐨𝐧 𝐅𝐚𝐜𝐞𝐛𝐨𝐨𝐤 👉🏻 https://lnkd.in/eWcXVwAt