mahdi mallaki

Brief summary of the Linux kernel parameters to optimize Kafka cluster performance also use XFS file system for Kafka cluster and disabling journaling can offer a performance boost, but it should be done cautiously, especially if data integrity is a top priority #tuning #Kafka cluster

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Comprehensive Guide to Setting up Grafana, Prometheus, and Loki #Grafana,#Prometheus,#Loki,#Kuberenetes,#Docker,#Monitoring

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𝐒𝐨, 𝐰𝐡𝐚𝐭 𝐢𝐬 𝐃𝐚𝐲 0, 𝐃𝐚𝐲 1, 𝐚𝐧𝐝 𝐃𝐚𝐲 2 𝐨𝐟 𝐊𝐮𝐛𝐞𝐫𝐧𝐞𝐭𝐞𝐬 𝐀𝐝𝐨𝐩𝐭𝐢𝐨𝐧? Day 0️⃣: Planning & Preparation - Define goals and requirements - Design architecture - Select tools and technologies Day 1️⃣: Deployment - Install and configure Kubernetes - Deploy initial workloads - Set up CI/CD pipelines Day 2️⃣: Operations & Maintenance - Monitor and manage the cluster - Scale applications - Perform regular updates and backups Adopting Kubernetes involves continuous planning, deployment, and maintenance to ensure a robust and scalable environment. #kubernetes #openshift #rosa #aro #aks #eks #gke

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Exploring Load Testing with Grafana k6: Setup and Real-World Performance Analysis

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Alhamdulillah, I passed the exams. Thanks to Allah and my wife, who support and grant these opportunities. A bit nervous on waiting for the result of this certification attempt. The questions in the exam cover many hidden and unusual technical knowledges on cloud native #prometheus and monitoring stack. #pca #linuxfoundation #sre #devops #devsecops #monitoring #grafana #victoriametrics

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Kafka vs RabbitMQ vs ActiveMQ RabbitMQ, Apache Kafka, and ActiveMQ are three popular messaging systems used in the industry. Apache Kafka is an open-source distributed event streaming platform that was originally developed by LinkedIn. RabbitMQ is an open-source message broker that implements the Advanced Message Queuing Protocol (AMQP) standard Apache ActiveMQ is an open-source message broker that implements the Java Message Service (JMS) API. ActiveMQ is written in Java and has a pluggable architecture that allows for easy extensibility. Lets find out the difference between them from messaging model to performance.Here are key differences between Apache Kafka, RabbitMQ and ActiveMQ:

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OpenShift -> Kubernetes++ OpenShift is a comprehensive enterprise Kubernetes platform developed by Red Hat. It builds on top of Kubernetes, adding a range of features and tools to simplify the deployment, management, and scaling of containerized applications. Here's a breakdown of what OpenShift is and how it enhances Kubernetes: 1. Kubernetes at Its Core   At its heart, OpenShift is powered by Kubernetes, which is an open-source system for automating the deployment, scaling, and management of containerized applications. Kubernetes provides the foundation for orchestrating and running containers, but OpenShift adds several additional capabilities that make it more user-friendly and robust for enterprises. 2. Built for Developers & Operations Teams   OpenShift bridges the gap between development and operations teams (often referred to as DevOps). It provides a platform where developers can build, deploy, and manage applications easily, while operations teams can manage the underlying infrastructure and ensure the system's reliability and security. 3. Simplified Deployment & Management   OpenShift simplifies the process of deploying applications by offering:   - Integrated CI/CD Pipelines: Built-in Continuous Integration/Continuous Deployment (CI/CD) tools help automate testing, building, and deployment processes.   - Source-to-Image (S2I): Developers can directly push their source code to OpenShift, and the platform will handle the containerization and deployment processes.   - Developer Tools: OpenShift provides an intuitive web console and CLI tools that allow developers to manage their apps with minimal Kubernetes knowledge. 4. Enhanced Security   OpenShift offers enterprise-grade security with features like:   - Role-Based Access Control (RBAC): Fine-grained access management for teams and users.   - Network Policies: Control over internal and external network traffic for security compliance.   - Automated Security Scanning: OpenShift includes security tools that continuously scan images for vulnerabilities and enforce security policies. 5. Multi-Cloud & Hybrid Cloud Support   OpenShift is designed to work across multiple cloud providers (like AWS, Azure, Google Cloud) and on-premises environments. It allows enterprises to run their applications consistently across any infrastructure, supporting hybrid cloud and multi-cloud strategies. 6. Operator Framework   OpenShift includes an Operator Framework, which automates the management of Kubernetes-native applications. Operators simplify the deployment and lifecycle management of complex applications by encapsulating human operational knowledge in software. 7. Red Hat Ecosystem   Being developed by Red Hat, OpenShift benefits from enterprise support, certified tools, and integrations with a wide range of technologies, from Ansible for automation to OpenStack for cloud infrastructure. #Openshift #Microservice #Knative #Kubernetes

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🌟  .𝙉𝑬𝙏 9.0.0-𝙥𝒓𝙚𝒗𝙞𝒆𝙬.5 𝒊𝙨 𝙣𝒐𝙬 𝙖𝒗𝙖𝒊𝙡𝒂𝙗𝒍𝙚! 🌟 .NET 9.0.0-preview.5 preview release offers early access to features in development, though it’s not recommended for production use. The SDK (v9.0.100-preview.5) is available for building apps across Linux, macOS, and Windows, supporting languages like C# 12.0, F# 8.0, and Visual Basic 16.9. For running apps, the ASP.NET Core Runtime 9.0.0-preview.5 enables existing web/server applications, and the .NET Desktop Runtime 9.0.0-preview.5 supports Windows desktop applications. Each runtime includes necessary components to run console apps, available for various OS configurations. 🖥️ For detailed information and downloads, visit the .NET 9.0 downloads page or check out the release notes. 🚀 https://lnkd.in/gWKupGDR

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𝗗𝗮𝘆 𝟮,𝟑,𝟰,𝟱,𝟲/𝟭𝟬𝟬 𝗼𝗳 𝗕𝘆𝘁𝗲𝘄𝗶𝘀𝗲 𝗙𝗲𝗹𝗹𝗼𝘄𝘀𝗵𝗶𝗽 - 𝗗𝗲𝘃𝗢𝗽𝘀 𝗧𝗿𝗮𝗰𝗸 🚀𝐉𝐨𝐮𝐫𝐧𝐞𝐲 𝐢𝐧𝐭𝐨 𝐭𝐡𝐞 𝐋𝐢𝐧𝐮𝐱 𝐖𝐨𝐫𝐥𝐝! 🚀 𝙆𝙚𝙮 𝙇𝙞𝙣𝙪𝙭 𝘾𝙤𝙣𝙘𝙚𝙥𝙩𝙨: ✅Kernel is a critical part of Linux that allocates resources and communicates with other hardware on your system. ✅ Package Manager as a tool to aid you with installing and managing software. ✅ Creating virtual machines and bundling of applications. 𝘼𝙧𝙘𝙝𝙞𝙩𝙚𝙘𝙩𝙪𝙧𝙚 𝘾𝙤𝙢𝙥𝙤𝙣𝙚𝙣𝙩𝙨: ✅ It helps in Booting the system. ✅ The System Libraries & GUI/CLI such as OS code literals, interfaces. ✅ Communication between processes and system files. 𝙉𝙚𝙩𝙬𝙤𝙧𝙠𝙞𝙣𝙜 𝙀𝙨𝙨𝙚𝙣𝙩𝙞𝙖𝙡𝙨: ✅ Networking Protocols such as TCP/IP, DHCP, DNS. ✅ It helps in secure remote connections and file transfer. ✅ It helps in aata governance in network firewalls and load balancers. 𝙎𝙮𝙨𝙩𝙚𝙢 𝘼𝙙𝙢𝙞𝙣𝙞𝙨𝙩𝙧𝙖𝙩𝙞𝙤𝙣: ✅ Monitoring and Backup Tools. ✅ User and disk Management for managing user accounts and disk space. ✅ Automation and Security such as Ansible, SELinux etc for automation and security purposes. 𝘼𝙙𝙫𝙖𝙣𝙘𝙚𝙙 𝘾𝙤𝙣𝙘𝙚𝙥𝙩𝙨: ✅ It includes resource management and isolation. ✅ It have different kinds of storage solutions, 𝙀𝙨𝙨𝙚𝙣𝙩𝙞𝙖𝙡 𝘾𝙤𝙢𝙢𝙖𝙣𝙙𝙨: ✅ It have Commands like ls, cd and pwd for traversing through directories. ✅ It helps in file manipulation using cp, mv and rm commands. ✅ It have FeaturePackage Management such as apt-get, yum, dnf for software installation. This learning journey has been is a impottant step forward in my DevOps path. I am looking forward to applying these skills in real-world scenarios and continuing my exploration in this DevOps track. Read more about my journey on: ✅ 𝗠𝗲𝗱𝗶𝘂𝗺 : https://lnkd.in/dmUzN-MQ ✅ 𝗛𝗮𝘀𝗵𝗻𝗼𝗱𝗲 : https://lnkd.in/dWtUTuKf #DevOps #ByteWiseFellowship #LearningJourney #CareerGrowth #100daysofbytewise #100daysofdevops Bytewise Limited Rashid Wassan

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🔍 𝐄𝐱𝐩𝐥𝐨𝐫𝐢𝐧𝐠 𝐋𝐢𝐧𝐮𝐱: 𝐂𝐨𝐦𝐦𝐚𝐧𝐝𝐬, 𝐅𝐢𝐥𝐞 𝐒𝐲𝐬𝐭𝐞𝐦𝐬, 𝐚𝐧𝐝 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 Linux is at the core of modern DevOps, powering servers, cloud platforms, and containerized applications. Mastering Linux commands, understanding the file system hierarchy, and exploring its architecture are fundamental skills for anyone navigating this field. To support this learning journey, I’ve compiled a comprehensive overview in my GitHub repository, covering: 🛠️ Essential Linux Commands 📂 Linux File System Hierarchy 🌐 Linux Architecture Basics This resource serves as an introduction to these vital concepts, with plans to expand on each in separate, detailed files over time. 👇🏾 Explore the full overview here: https://lnkd.in/ejCcQSz3 #Linux #DevOps #GitHub #OpenSource

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🚀 Mastering Prometheus Architecture 🚀 As a DevOps Engineer, having the right monitoring tools is essential to keep systems running smoothly. One tool I’ve found invaluable is Prometheus, which offers a powerful, customizable approach to data collection and system monitoring. Let’s break down the core components of Prometheus and how they all work together. 1. High-Level Prometheus Architecture: Think of Prometheus as a central hub, collecting data from various sources using a pull-based model. It actively scrapes metrics at regular intervals to keep tabs on system health. 2. Prometheus Server: The brains of the operation—scraping, storing, and letting us analyze data with PromQL. 3. Time-Series Database (TSDB): Where all that valuable data is stored, helping us track performance trends and historical patterns. 4. Targets: The data sources, including servers, applications, and anything exposing metrics. 5. Exporters: Translators that make non-Prometheus metrics readable, allowing us to monitor a wide range of sources. 6. Service Discovery: For large systems, service discovery automates the process of finding and scraping new targets, simplifying scale. 7. Pushgateway: Perfect for short-lived jobs where direct scraping isn’t possible, allowing temporary data storage for Prometheus to retrieve later. 8. Client Libraries: Simplifies adding monitoring to applications by making metrics available in the Prometheus-friendly format. 9. Alertmanager: Prometheus handles the monitoring, and the Alertmanager takes care of notifying us. It groups and sends alerts to email, chat, or other preferred channels. 10. PromQL: The specialized query language to analyze stored data, ask targeted questions, and get insights into performance. By understanding how these pieces fit together, you can leverage Prometheus to ensure your systems run smoothly and reliably. #DevOps #Prometheus #CloudComputing #Monitoring #MyceliumWeb #TechCommunity #CloudEngineer #InfrastructureManagement #PromQL #Alerting

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🚀 Enhancing Kubernetes Cluster Performance with Descheduler 🌐 In dynamic Kubernetes environments, maintaining optimal resource utilization and performance can be challenging. One powerful tool to address this is the Kubernetes Descheduler. Here’s how you can leverage it to improve your cluster efficiency: 1. Pod Eviction Based on Node Conditions: The Descheduler can evict pods from nodes that are under high resource pressure or have specific taints. This helps to redistribute workloads and ensure more balanced resource utilization across your cluster. 2. Node Affinity and Anti-Affinity: If your nodes have specific labels or taints, the Descheduler can help in rebalancing pods according to the updated affinity/anti-affinity rules, ensuring that your workload placement adheres to your intended topology. 3. Resource Defragmentation: Over time, the allocation of resources across your nodes can become fragmented. The Descheduler helps in redistributing pods to defragment resource usage, making it easier to accommodate new pods and scale applications efficiently. 4. Evicting Pods Based on Age: For clusters with long-running workloads, some pods may become stale or less optimal for the current state of the cluster. The Descheduler can evict these older pods to make room for newer, more efficient deployments. 5. Evicting Pods with Noisy Neighbors: If certain pods are causing performance degradation for others on the same node (the "noisy neighbor" problem), the Descheduler can help by evicting and redistributing these pods to maintain overall cluster performance. Implementing the Descheduler can lead to significant improvements in your Kubernetes cluster management by continuously optimizing pod placement and resource utilization. 🔗 Learn more about the Kubernetes Descheduler and how to implement it: What strategies do you use to optimize your Kubernetes clusters? Share your experiences and tips below! 💡 🔁 Consider a Repost if this is useful. #Kubernetes #Descheduler #CloudComputing #DevOps #ClusterManagement #TechTips #Automation #DevOpsEngineer

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This article showcases how hashtag#OpenTelemetry hashtag#java hashtag#quarkus OTel instrumentation is a game-changer in developing distributed applications faster and with higher quality. Can’t make it in person to hashtag#splunkconf24? We've got you covered 🙌🏼 https://bit.ly/3zONePx

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𝗧𝗖𝗦 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 : 𝟏. 𝐖𝐡𝐚𝐭 𝐜𝐨𝐦𝐦𝐚𝐧𝐝 𝐰𝐢𝐥𝐥 𝐲𝐨𝐮 𝐠𝐢𝐯𝐞 𝐟𝐨𝐫 𝐢𝐦𝐩𝐨𝐫𝐭𝐢𝐧𝐠 𝐝𝐚𝐭𝐚 𝐟𝐫𝐨𝐦 𝐚 𝐏𝐚𝐫𝐪𝐮𝐞𝐭 𝐟𝐢𝐥𝐞 𝐮𝐬𝐢𝐧𝐠 𝐒𝐪𝐨𝐨𝐩? sqoop import \ --connect jdbc:hive2://<hive-server-host>:<port>/<hive-database> \ --username <hive-username> \ --password <hive-password> \ --table <hive-table-name> \ --from-parquet <path/to/parquet/file> \ [other Sqoop options] 2. How can you determine whether a job has succeeded or failed, and where can you find the error messages? 𝟑. 𝐖𝐡𝐞𝐫𝐞 𝐢𝐬 𝐭𝐡𝐞 𝐇𝐢𝐯𝐞 𝐦𝐞𝐭𝐚𝐬𝐭𝐨𝐫𝐞 𝐬𝐭𝐨𝐫𝐞𝐝, 𝐚𝐧𝐝 𝐰𝐡𝐚𝐭 𝐢𝐬 𝐭𝐡𝐞 𝐝𝐞𝐟𝐚𝐮𝐥𝐭 𝐬𝐭𝐨𝐫𝐚𝐠𝐞 𝐦𝐞𝐜𝐡𝐚𝐧𝐢𝐬𝐦? Default Location: The Hive metastore is typically stored in a relational database management system (RDBMS) like MySQL. The exact location depends on your configuration. Check the hive-site.xml file on your Hive server for settings like javax.jdo.option.ConnectionURL. Default Storage Mechanism: By default, Hive uses a relational database (usually configured in hive-site.xml) to store metadata about tables, partitions, and other schema information. 𝟒. 𝐖𝐡𝐚𝐭 𝐚𝐫𝐞 𝐭𝐡𝐞 𝐭𝐲𝐩𝐞𝐬 𝐨𝐟 𝐣𝐨𝐢𝐧𝐬 𝐚𝐯𝐚𝐢𝐥𝐚𝐛𝐥𝐞 𝐢𝐧 𝐇𝐢𝐯𝐞, 𝐚𝐧𝐝 𝐜𝐚𝐧 𝐲𝐨𝐮 𝐩𝐫𝐨𝐯𝐢𝐝𝐞 𝐭𝐡𝐞 𝐬𝐲𝐧𝐭𝐚𝐱 𝐟𝐨𝐫 𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐢𝐧𝐠 𝐚 𝐣𝐨𝐢𝐧? Hive supports various join types similar to SQL: Inner Join: Returns rows where the join condition is met in both tables. SQL Left Outer Join: Returns all rows from the left table and matching rows from the right table. If there's no match in the right table, null values are filled for right table columns.  Right Outer Join: Similar to left outer join, but returns all rows from the right table.  Full Outer Join: Returns all rows from both tables, even if there's no match in the join condition. Null values are filled for non-matching columns. 𝟓. 𝐇𝐨𝐰 𝐜𝐚𝐧 𝐲𝐨𝐮 𝐝𝐞𝐥𝐞𝐭𝐞 𝐨𝐫 𝐮𝐩𝐝𝐚𝐭𝐞 𝐬𝐩𝐞𝐜𝐢𝐟𝐢𝐜 𝐝𝐚𝐭𝐚 𝐢𝐧 𝐇𝐢𝐯𝐞, 𝐚𝐧𝐝 𝐢𝐬 𝐢𝐭 𝐟𝐞𝐚𝐬𝐢𝐛𝐥𝐞 𝐭𝐨 𝐝𝐞𝐥𝐞𝐭𝐞 𝐨𝐫 𝐮𝐩𝐝𝐚𝐭𝐞 𝐢𝐧𝐝𝐢𝐯𝐢𝐝𝐮𝐚𝐥 𝐫𝐨𝐰𝐬 𝐨𝐟 𝐝𝐚𝐭𝐚 𝐮𝐬𝐢𝐧𝐠 𝐇𝐢𝐯𝐞 𝐐𝐮𝐞𝐫𝐲 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 (𝐇𝐐𝐋)? Delete: While Hive doesn't directly delete individual rows, you can use techniques like: Filtering with WHERE: Delete rows based on specific conditions. DELETE FROM table_name WHERE condition; Dropping Partitions: If your table is partitioned, you can drop partitions to remove unwanted data. This is faster than deleting all data and reloading. Update: Hive is not optimized for real-time updates. Consider using Apache Spark or other frameworks for row-level updates in large datasets. However, you can update entire columns or partitions with techniques like: ALTER TABLE ... SET ...: Update column values for all rows. INSERT OVERWRITE ...: Overwrite existing data based on the specified condition. #Bigdata Talks with Karthik K. & Seekho Bigdata Institute Tagged for Better Reach : Sivakumar Babuji

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🎻 𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 𝘃𝘀. 𝗥𝗲𝗮𝗹 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮 🎶 ==================================== 🎩 𝗖𝗼𝗻𝗱𝘂𝗰𝘁𝗼𝗿 𝘃𝘀. 𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀: The conductor 🎼 leads musicians, just like Kubernetes 🛠️ manages containers. 🎻 𝗠𝘂𝘀𝗶𝗰𝗶𝗮𝗻𝘀 𝘃𝘀. 𝗖𝗼𝗻𝘁𝗮𝗶𝗻𝗲𝗿𝘀: Musicians play different instruments 🎷, while Kubernetes manages different containers 🐳 for each part of the application. ⏱️ 𝗖𝗼𝗼𝗿𝗱𝗶𝗻𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗧𝗶𝗺𝗶𝗻𝗴: The conductor ensures perfect timing ⏲️ and harmony 🎶; Kubernetes ensures containers run smoothly 🏃‍♂️ and scale appropriately 📈. 🎺 𝗦𝗰𝗮𝗹𝗶𝗻𝗴: More musicians can be added for a bigger performance 🎺, just like Kubernetes scales containers 🚀 based on demand. 🛠️ 𝗙𝗮𝗶𝗹𝘂𝗿𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁: When something goes wrong ❌, the conductor adjusts the performance 🎵; Kubernetes automatically restarts failed containers 🔄. 📊 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴: The conductor ensures real-time balance ⚖️ in music, while Kubernetes monitors container health and adjusts resources accordingly 🖥️. 🎼 𝗛𝗮𝗿𝗺𝗼𝗻𝘆/𝗕𝗮𝗹𝗮𝗻𝗰𝗲: The conductor creates musical harmony 🎶; Kubernetes ensures all containers work together seamlessly 🤝 to deliver the application. #Kubernetes #DevOps #CNCF #CloudNative #GoogleCloud #StartupTech #ProductManagement #TechInnovation #ContainerOrchestration #CloudComputing #SaaS #PlatformEngineering #TechLeadership #DigitalTransformation #Microservices #TechStartups #Orchestration #Serverless #Automation #CloudInfrastructure #GoogleKubernetesEngine #Google #tcs #piyushsachadeva

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Exploration | Mounting Container Images As Volumes in kubernetes 1.31 The Kubernetes community is moving towards fulfilling more Artificial Intelligence (AI) and Machine Learning (ML) use cases in the future. While the project has been designed to fulfill microservice architectures in the past, it’s now time to listen to the end users and introduce features which have a stronger focus on AI/ML. #Devops #MLOps #AI #kubernetes

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#Junior #DevOps #Engineer VS #Senior #DevOps #Engineer #Jr. Terraform Code: 𝐫𝐞𝐬𝐨𝐮𝐫𝐜𝐞 "aws_instance" "app_server" {  ami      = var.ami_id  instance_type = var.instance_type  tags = {   Name = "App Server"  } } #Sr. Terraform Code: 𝐦𝐨𝐝𝐮𝐥𝐞 "app_server" {  source    = "./𝐦𝐨𝐝𝐮𝐥𝐞𝐬/aws_instance"  ami      = var.ami_id  instance_type = var.instance_type  name     = "App Server" } Though both codes work... But why is the latter better? - Keeps things modular - Easier to reuse and maintain - Cuts down on duplicate code I used Jr/Sr here for relatability, but I prefer entry-level and experienced. By no means does Sr = yrs of experience. A senior codes for what’s next, not just what’s now. Be that senior. 👍

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