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Andrey L., Data Engineer

Sao Paulo, Brazil
Last Updated: 4 Jul 2023

- 9+ years of experience as a development and architecture of Big Data solutions. - Advanced English - Available ASAP

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Alexander I., Full Stack Software Engineer ( PHP/WordPress/HTML )

Ukraine
Last Updated: 4 Jul 2023

- 5 years of commercial experience in Web Development - Зку-Intermediate English

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PHP

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Svitlana I., Frontend Engineer, Angular

Lviv, Ukraine
Last Updated: 4 Jul 2023

- 10 years of work experience with frontend development - Strong knowledge of Typescript - Ensuring the high performance web applications across all platforms, including desktop and mobile - Writing tested, idiomatic, and documented elements of JavaScript, HTML, and CSS - Coordinating the workflow between the Angular developers and the HTML coder and graphic designer - Cooperating with the backend developers while building the REST fuI API - Websockets - Staying in close communication with external web services - Closely worked with UI/UX designers, Wireframing - Using common methodologies (KISS, DRY, SOLID)

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Syed S., Salesforce Developer

Pakistan
Last Updated: 4 Jul 2023

- Software Developer with 18 years of experience - Software Development Life Cycle professional background Agile/Scrum - Programming Knowledge & experience, System Migrations/Integrations, Cross Functional Skills - intermediate English - Available ASAP - No vacation plans for next 2 months

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Salesforce

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Anatoliy Sh, Full Stack Ruby on Rails Developer

Ukraine
Last Updated: 4 Jul 2023

- Has 10+ years of software development experience. - Worked on 4 projects for 3 years using my Ruby skills as a Full Stack Developer. - Has 3 years of experience as a tech lead in a Front-end team. - Experience in creating architectural solutions, code optimization and verification. He also knows how to create new features, fix bugs, implement design and user interface, as well as establish interaction between the Front-end and Back-end parts. - Advanced English - Available ASAP

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Table of Contents

TOP 10 Facts about Yarn

  • Yarn is a long continuous strand of fibers that is used in various textile industries for knitting, weaving, and sewing.
  • The word “yarn” is derived from the Old English word “gearn,” which means “spun fiber.”
  • Yarn can be made from various materials, including natural fibers like wool, cotton, silk, and linen, as well as synthetic fibers like acrylic, polyester, and nylon.
  • There are different yarn weights or thicknesses available, categorized by a numbering system ranging from lace weight (the thinnest) to super bulky (the thickest).
  • The weight of a yarn determines its recommended needle or hook size and the finished fabric’s drape and texture.
  • Yarn comes in a wide array of colors, dyed using various techniques such as solid, variegated, self-striping, and hand-painted.
  • Yarn can be plied, meaning multiple strands are twisted together, which enhances its strength and durability.
  • Crochet and knitting are the most popular crafts that utilize yarn as the primary material.
  • Yarn can be used for a variety of projects, including clothing, accessories, home decor items, and even artwork.
  • The global yarn market is expected to reach a value of over $12 billion by 2025, driven by the growing demand for textile products worldwide.

TOP 10 Yarn Related Technologies

  • Languages (JavaScript)

    JavaScript is the go-to language for Yarn software development. It is a versatile and widely adopted language that enables developers to build interactive and dynamic web applications. JavaScript is supported by all major browsers and has a vast ecosystem of libraries and frameworks.

  • Package Managers (Yarn)

    Yarn is a popular package manager for JavaScript projects. It offers faster and more reliable package installation compared to its counterparts. Yarn manages dependencies efficiently and provides features like caching, parallelism, and offline mode, making it an ideal choice for Yarn software development.

  • Frameworks (React)

    React is a powerful JavaScript framework widely used for building user interfaces. It follows a component-based architecture and offers a virtual DOM for efficient rendering. React’s popularity stems from its simplicity, reusability, and extensive ecosystem of tools and libraries.

  • Build Tools (Webpack)

    Webpack is a popular build tool used in Yarn software development. It helps bundle and optimize JavaScript modules, CSS, and other assets for deployment. Webpack’s ability to handle complex module dependencies and support for code splitting make it an essential tool for building modern web applications.

  • Testing Frameworks (Jest)

    Jest is a widely adopted testing framework for JavaScript applications. It provides a simple and intuitive API for writing tests and offers features like test coverage reporting, mocking, and snapshot testing. Jest’s ease of use and extensive documentation make it a preferred choice for Yarn software developers.

  • Version Control Systems (Git)

    Git is the de facto version control system for Yarn software development. It enables developers to track changes, collaborate, and manage code efficiently. Git’s distributed nature, branching model, and support for merging make it indispensable for software development projects of any scale.

  • Integrated Development Environments (VS Code)

    Visual Studio Code (VS Code) is a popular code editor among Yarn software developers. It offers a rich set of features like intelligent code completion, debugging, version control integration, and an extensive marketplace of extensions. VS Code’s lightweight and customizable nature make it a preferred choice for efficient Yarn software development.

What are top Yarn instruments and tools?

  • Apache Hadoop YARN: Apache Hadoop YARN (Yet Another Resource Negotiator) is a cluster management technology in the Apache Hadoop ecosystem. It was introduced in Hadoop 2.0 and has become the de facto standard for resource management in big data processing. YARN allows multiple data processing engines such as MapReduce, Apache Spark, and Apache Flink to run on a shared Hadoop cluster, enabling more efficient resource utilization.
  • Apache Tez: Apache Tez is an extensible framework built on top of YARN that allows for high-performance batch and interactive data processing. It provides a directed acyclic graph (DAG) execution model, which enables complex data processing workflows with efficient resource management. Tez has gained popularity for its ability to accelerate Hive and Pig queries by optimizing the execution plan.
  • Apache Slider: Apache Slider is a YARN application that allows users to deploy existing distributed applications on a Hadoop cluster without modification. It simplifies the process of integrating and managing non-YARN applications by providing a framework for packaging, deploying, and monitoring these applications within the YARN ecosystem.
  • Apache Hadoop Ozone: Apache Hadoop Ozone is a scalable and distributed object storage system built on top of YARN. It provides a highly available and fault-tolerant storage solution for big data applications. Ozone is designed to handle massive amounts of unstructured data and offers features such as multi-tenancy, data durability, and data consistency.
  • Apache Hadoop Timeline Service (ATS): Apache Hadoop Timeline Service is a component of YARN that provides a central repository for storing and retrieving information about the execution of applications on a Hadoop cluster. ATS enables developers and administrators to track the progress of jobs, analyze resource utilization, and diagnose performance issues in a distributed environment.
  • Apache Hadoop Capacity Scheduler: Apache Hadoop Capacity Scheduler is a pluggable scheduler for YARN that allows for fine-grained allocation of cluster resources based on predefined capacity limits. It supports multiple queues with different priorities, allowing organizations to allocate resources based on business requirements and workload characteristics.
  • Apache Hadoop Fair Scheduler: Apache Hadoop Fair Scheduler is another pluggable scheduler for YARN that provides fair sharing of cluster resources among multiple applications. It ensures that all applications get a fair share of resources based on their demands and priorities. The fair scheduler is commonly used in multi-tenant environments where fairness and isolation are crucial.
  • Apache Hadoop Distributed Shell (HDS): Apache Hadoop Distributed Shell is a framework for running distributed applications on a Hadoop cluster. It allows users to submit and execute arbitrary applications as YARN applications, providing a flexible and secure environment for running diverse workloads.

Let’s consider Difference between Junior, Middle, Senior, Expert/Team Lead developer roles.

Seniority NameYears of experienceResponsibilities and activitiesAverage salary (USD/year)
Junior0-2 yearsAssisting senior developers in coding, testing, and debugging software. Participating in code reviews and learning best practices. Developing basic features and functionality under supervision. Collaborating with the team to deliver software projects.$50,000 – $70,000
Middle2-5 yearsDeveloping more complex features independently. Taking ownership of assigned tasks and projects. Mentoring junior developers. Participating in code architecture discussions. Conducting code reviews. Collaborating with cross-functional teams for project delivery.$70,000 – $90,000
Senior5-10 yearsDesigning and implementing complex software solutions. Leading projects and making technical decisions. Mentoring and guiding junior and middle developers. Ensuring code quality and performance. Collaborating with stakeholders to define project requirements and deliverables.$90,000 – $120,000
Expert/Team Lead10+ yearsLeading a team of developers. Setting technical direction and making strategic decisions. Providing guidance and mentorship to the team. Collaborating with other teams and stakeholders to align technical solutions with business goals. Ensuring high-quality code, performance, and scalability. Driving innovation and process improvement.$120,000 – $150,000+

Hard skills of a Yarn Developer

Hard skills of a Yarn Developer:

Junior

  • JavaScript: Proficiency in JavaScript programming language, including ES6+ features and syntax.
  • Yarn Package Manager: Understanding of Yarn package manager, its commands, and how to manage dependencies.
  • Version Control: Knowledge of Git version control system and basic commands for code repository management.
  • Debugging: Ability to identify and fix common bugs and issues in Yarn configurations and scripts.
  • Package Publishing: Familiarity with publishing packages to package registries like npm or Yarn’s own registry.

Middle

  • Yarn Workspaces: Experience in setting up and managing Yarn workspaces for monorepo-style development.
  • Continuous Integration/Continuous Deployment (CI/CD): Understanding of CI/CD pipelines and integration with Yarn for automated builds and deployments.
  • Performance Optimization: Knowledge of techniques for optimizing Yarn performance, such as caching and parallelization.
  • Scripting: Proficiency in scripting languages like Bash or PowerShell for automating Yarn-related tasks.
  • Security: Awareness of common security vulnerabilities and best practices for securing Yarn dependencies.
  • Testing: Familiarity with testing frameworks and libraries for unit and integration testing of Yarn packages.
  • Code Review: Ability to conduct and participate in code reviews to ensure code quality and adherence to best practices.

Senior

  • Yarn Plugin Development: Experience in developing custom Yarn plugins to extend its functionality.
  • Performance Tuning: Advanced knowledge of optimizing Yarn performance through advanced configuration and tuning.
  • Infrastructure as Code (IaC): Understanding of infrastructure automation tools like Terraform or CloudFormation for managing Yarn deployment infrastructure.
  • Containerization: Proficiency in containerization technologies like Docker for packaging and deploying Yarn applications.
  • Scaling and Load Balancing: Experience in scaling Yarn clusters and implementing load balancing strategies for high availability.
  • Monitoring and Logging: Knowledge of monitoring and logging tools to ensure the health and performance of Yarn applications.
  • Troubleshooting: Ability to diagnose and resolve complex issues in Yarn deployments and configurations.
  • Team Leadership: Strong leadership skills to guide and mentor junior developers, and collaborate effectively with cross-functional teams.

Expert/Team Lead

  • Architecture Design: Ability to design scalable and resilient Yarn architectures for large-scale applications or enterprise environments.
  • Performance Analysis: Expertise in analyzing and optimizing performance bottlenecks in Yarn applications and infrastructure.
  • DevOps: Proficiency in DevOps practices and tools, such as Jenkins, Kubernetes, or Ansible, for automating Yarn deployment workflows.
  • Cloud Computing: Experience in deploying and managing Yarn applications on cloud platforms like AWS, Azure, or GCP.
  • Security Audits: Ability to perform security audits and vulnerability assessments for Yarn deployments.
  • Project Management: Strong project management skills to lead Yarn development projects from inception to delivery.
  • Technical Documentation: Excellent documentation skills to create comprehensive technical documentation for Yarn projects.
  • Mentoring and Training: Ability to mentor and train team members on Yarn best practices and advanced concepts.
  • Collaboration and Communication: Effective communication and collaboration skills to work with stakeholders and cross-functional teams.
  • Problem Solving: Expert problem-solving skills to tackle complex Yarn-related challenges and find innovative solutions.
  • Industry Knowledge: Continuous learning and staying updated with the latest trends and advancements in Yarn and related technologies.

Soft skills of a Yarn Developer

Soft skills are essential for success in any profession, and being a Yarn Developer is no exception. In addition to technical expertise, Yarn Developers also require a set of soft skills to effectively collaborate with team members, communicate ideas, and solve problems. Here are the key soft skills for Yarn Developers at different levels:

Junior

  • Attention to detail: Being meticulous in examining yarn specifications and ensuring accurate implementation.
  • Time management: Prioritizing tasks and meeting deadlines to ensure timely delivery of yarn projects.
  • Teamwork: Collaborating effectively with other developers, designers, and stakeholders to achieve project goals.
  • Communication: Clearly conveying ideas and updates to team members, ensuring smooth workflow.
  • Adaptability: Quickly adapting to changes in project requirements and adjusting the yarn development approach accordingly.

Middle

  • Problem-solving: Identifying and resolving complex issues that may arise during the yarn development process.
  • Leadership: Taking ownership of tasks, guiding junior developers, and providing mentorship to help them grow.
  • Critical thinking: Analyzing yarn development challenges from different angles to find optimal solutions.
  • Collaboration: Effectively working with cross-functional teams, such as product managers and quality assurance, to ensure seamless integration.
  • Effective feedback: Providing constructive feedback to team members to improve their yarn development skills and processes.
  • Adaptability: Adapting to new technologies and industry trends to enhance yarn development practices.
  • Time management: Managing multiple yarn projects simultaneously and ensuring efficient resource allocation.

Senior

  • Strategic thinking: Aligning yarn development strategies with overall business objectives and long-term vision.
  • Project management: Overseeing the end-to-end yarn development lifecycle, including planning, execution, and delivery.
  • Conflict resolution: Effectively resolving conflicts within the team and facilitating a positive work environment.
  • Innovation: Identifying opportunities for process improvement and implementing innovative yarn development practices.
  • Technical leadership: Demonstrating expertise in yarn development technologies and providing guidance to the team.
  • Client management: Building strong relationships with clients, understanding their requirements, and delivering solutions that meet their needs.
  • Emotional intelligence: Understanding and managing emotions to foster effective communication and collaboration.
  • Mentorship: Mentoring junior and middle-level developers, sharing knowledge and helping them advance in their careers.

Expert/Team Lead

  • Strategic planning: Developing long-term yarn development strategies and roadmaps to drive business growth.
  • Team management: Leading and managing a team of yarn developers, ensuring high performance and productivity.
  • Decision-making: Making informed decisions based on extensive experience and domain knowledge.
  • Influence: Persuading stakeholders and gaining buy-in for yarn development initiatives and projects.
  • Continuous improvement: Implementing processes and practices to continuously enhance yarn development efficiency and quality.
  • Business acumen: Understanding the business context and aligning yarn development efforts to support organizational goals.
  • Thought leadership: Staying updated with industry trends, sharing insights, and contributing to the yarn development community.
  • Negotiation: Negotiating contracts, timelines, and resources to ensure successful yarn project delivery.
  • Risk management: Identifying and mitigating risks associated with yarn development projects to minimize potential issues.
  • Strategic partnerships: Collaborating with external partners and vendors to leverage their expertise and resources.
  • Empathy: Understanding the needs and perspectives of team members, fostering a supportive and inclusive work environment.

How and where is Yarn used?

Case NameCase Description
NetflixNetflix, a leading streaming platform, utilizes Yarn for job scheduling and resource management. With Yarn, Netflix can efficiently manage their vast amount of data and distribute workloads across their cluster. Yarn’s ability to dynamically allocate resources ensures optimal utilization and improved performance for Netflix’s data processing tasks.
FacebookFacebook leverages Yarn for managing their Hadoop workloads. By using Yarn, Facebook can efficiently allocate resources to various applications, ensuring fair sharing of cluster resources. Yarn also allows Facebook to handle diverse workloads, including batch processing, interactive queries, and real-time data processing, providing scalability and flexibility to their data infrastructure.
TwitterTwitter utilizes Yarn to manage their data processing tasks efficiently. Yarn enables Twitter to dynamically allocate resources based on workload demands, ensuring optimal resource utilization. With Yarn, Twitter can process massive amounts of data in real-time, enabling them to deliver timely insights and improve their overall user experience.
LinkedInLinkedIn relies on Yarn for managing their big data workloads effectively. Yarn enables LinkedIn to allocate resources dynamically, ensuring efficient utilization of their cluster. By leveraging Yarn’s capabilities, LinkedIn can process and analyze large volumes of data, enabling them to gain valuable insights and make data-driven decisions.
UberUber uses Yarn for resource management and job scheduling in their data infrastructure. Yarn allows Uber to efficiently allocate resources for their various data processing tasks, ensuring optimal utilization of their cluster. With Yarn, Uber can handle large-scale data processing, enabling them to deliver reliable and efficient services to their customers.
AirbnbAirbnb utilizes Yarn for managing their data-intensive workloads. Yarn’s resource management capabilities enable Airbnb to allocate resources based on workload demands, ensuring efficient utilization of their cluster. With Yarn, Airbnb can process and analyze vast amounts of data, enabling them to improve their recommendation systems and enhance their user experience.
Amazon Web Services (AWS)AWS offers Yarn as a managed service called Amazon EMR (Elastic MapReduce). Yarn’s resource management and job scheduling capabilities are leveraged by AWS customers to process and analyze large datasets on the cloud. With Yarn on AWS EMR, users can easily scale their data processing workloads and benefit from the scalability, reliability, and cost-effectiveness of the cloud.
WalmartWalmart leverages Yarn for managing their big data workloads efficiently. Yarn enables Walmart to allocate resources dynamically, ensuring optimal utilization of their cluster. By utilizing Yarn’s capabilities, Walmart can process and analyze large volumes of data, enabling them to gain valuable insights for improving their operations and customer experience.
PinterestPinterest uses Yarn for managing their data processing tasks effectively. Yarn’s resource management capabilities enable Pinterest to allocate resources based on workload demands, ensuring efficient utilization of their cluster. With Yarn, Pinterest can process and analyze vast amounts of data, enabling them to personalize user recommendations and enhance their platform’s engagement.
AdobeAdobe relies on Yarn for managing their big data workloads efficiently. Yarn’s resource allocation and job scheduling capabilities enable Adobe to process and analyze large datasets across their cluster. With Yarn, Adobe can handle complex data processing tasks, such as image and video analysis, enabling them to deliver innovative solutions to their customers.

Cases when Yarn does not work

  1. Compatibility Issues: Yarn may not work properly in certain environments or with specific versions of Node.js. For example, if you are using an older version of Node.js that is not supported by Yarn, you may encounter compatibility issues.
  2. Network Connectivity Problems: Yarn heavily relies on network connectivity to fetch dependencies from package registries. If you have a slow or unreliable internet connection, Yarn may not be able to download packages efficiently, resulting in failed installations or timeouts.
  3. Firewall or Proxy Restrictions: If your network has strict firewall rules or is behind a proxy server that restricts outgoing connections, Yarn may encounter difficulties in accessing package registries or downloading dependencies.
  4. Incorrect Configuration: Yarn requires proper configuration to function correctly. If you have misconfigured your Yarn settings or package.json file, it can lead to unexpected behavior or errors during installation or execution.
  5. Insufficient System Resources: Yarn, like any other software, requires sufficient system resources to run smoothly. If your system does not meet the minimum requirements or is under heavy load, Yarn may not work as expected and could experience performance issues.
  6. Conflicts with Other Package Managers: If you have multiple package managers installed on your system, such as npm or pnpm, conflicts may arise when using Yarn. These conflicts can result in dependency resolution problems or unexpected behavior.
  7. Bugs and Software Issues: Like any software, Yarn is not immune to bugs or software issues. Although the Yarn team strives to provide a stable and reliable tool, there may be instances where Yarn does not work due to undiscovered bugs or unresolved issues.

Pros & cons of Yarn

7 Pros of Yarn

  • Improved Performance: Yarn offers faster and more efficient package installations compared to other package managers. It utilizes parallel downloads and caching to speed up the process.
  • Security: Yarn provides enhanced security features such as checksum verification and integrity checks for packages. This helps to mitigate the risk of installing malicious or compromised packages.
  • Offline Mode: Yarn allows developers to work offline by leveraging its offline caching capabilities. It stores packages locally, enabling developers to install dependencies without an internet connection.
  • Resilience: Yarn has robust error handling mechanisms that make it more resilient to network failures and package installation issues. It automatically retries failed requests, reducing the likelihood of build failures.
  • Workspaces: Yarn supports workspaces, which enable developers to manage multiple packages within a single root project. This simplifies the development process for projects with complex dependencies.
  • Yarn Plug-ins: Yarn has a vibrant ecosystem of plug-ins that extend its functionality. These plug-ins offer additional features and integration with other development tools, enhancing the overall developer experience.
  • Compatibility: Yarn is compatible with both npm and Bower, allowing developers to seamlessly use packages from different ecosystems and migrate between package managers if needed.

7 Cons of Yarn

  • Learning Curve: Yarn has a slightly steeper learning curve compared to traditional package managers like npm. Developers may need to familiarize themselves with the new commands and workflows.
  • Memory Consumption: Yarn consumes more memory compared to npm, especially when dealing with large projects or a significant number of dependencies. This can be a concern for developers with limited system resources.
  • Dependency Bloat: In some cases, Yarn may install redundant or unnecessary dependencies, leading to increased project size. Developers need to carefully manage their dependencies to avoid unnecessary bloat.
  • Community Size: While Yarn has gained popularity, its community size is still smaller compared to npm. This means there might be fewer resources, tutorials, and community support available for specific issues.
  • Compatibility Issues: Although Yarn is compatible with npm, there can still be occasional compatibility issues when using certain packages or specific versions. Developers may need to troubleshoot and find workarounds for such issues.
  • Package Versioning: Yarn’s approach to package versioning differs slightly from npm, which can lead to conflicts or confusion when working with projects that use both package managers.
  • Dependency Locking: While Yarn’s lock file ensures consistent installations, it can also result in slower updates for packages. Developers may need to manually update the lock file to benefit from the latest package versions.

TOP 10 Tech facts and history of creation and versions about Yarn Development

  • Yarn, a package manager for JavaScript, was created by Facebook in 2016 as an alternative to npm.
  • Yarn was developed to address the shortcomings of npm, such as slow installation and inconsistent dependency resolution.
  • The inspiration for Yarn came from Facebook’s need to manage the vast number of JavaScript packages used in their projects.
  • Yarn introduced a new caching mechanism that significantly improved installation speed by leveraging previously downloaded packages.
  • One of the key features of Yarn is deterministic dependency resolution, ensuring that the same package versions are installed across different machines.
  • Yarn utilizes a lockfile called yarn.lock, which guarantees that the exact same dependencies are installed regardless of external factors.
  • Yarn introduced workspaces, allowing multiple packages to be managed together in a single repository, simplifying monorepo setups.
  • Yarn has a robust plugin system, enabling developers to extend its functionality and integrate it with other tools.
  • Yarn has steadily gained popularity since its release and is widely used in the JavaScript ecosystem.
  • Yarn has released several major versions, with each iteration introducing new features, performance improvements, and bug fixes.

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