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Its fundamental idea is to split up the functionalities of resource management and job scheduling/monitoring into separate daemons. The idea is to have a global ResourceManager (RM) and per-application ApplicationMaster (AM).
我们掌握了关于使用YARN Hadoop的7家公司数据。这个精心策划的名单可以下载,并附带了重要的公司具体信息,包括行业分类、组织规模、地理位置、融资轮次和收入数据等。
公司 | 国家 | 行业 | 雇员 | 收入 |
---|---|---|---|---|
![]() Notion | 美国 | Software Development | 3.9K | $30M |
Go2Andaman | 印度 | Travel Arrangements | 4 | $3.1M |
Quental | 西班牙 | It Services And It Consulting | 281 | |
![]() Basis Technologies | 美国 | Advertising Services | 1.4K | $520M |
![]() Airbnb | 美国 | Software Development | 38K | $6B |
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注册并下载完整的 7 家公司的列表
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技术使用统计数据和市场份额
您可以通过筛选地理位置、行业、公司规模、收入、技术使用情况、职位等来根据您的需求定制这些数据。您可以以Excel或CSV格式下载数据。
您可以获得有关此数据的提醒。您可以通过选择您感兴趣的技术来开始,然后当有新公司使用该技术时,您将会在您的收件箱中收到提醒。
您可以将这些数据导出到一个Excel文件,然后导入到您的CRM中。您也可以将这些数据导出到API。
YARN Hadoop 被用于 3 个国家
有 10 个 YARN Hadoop 替代品
常见问题
我们的数据来自于从数百万家公司收集的招聘信息。我们在公司网站、招聘平台和其他招聘平台上监控这些招聘信息。分析招聘信息提供了一种可靠的方法来了解公司正在使用的技术,包括他们使用的内部工具。
我们每天更新数据,以确保您访问的是最新的可用信息。这一频繁的更新过程保证了我们的洞察力和情报反映了行业内的最新发展和趋势。
YARN (Yet Another Resource Negotiator) is a cluster management technology that plays a fundamental role in Apache Hadoop. It is designed to manage resources and schedule tasks efficiently in a distributed environment. YARN serves as the architectural center of Hadoop, separating the resource management and job scheduling/monitoring functions, enabling Hadoop to support more varied processing approaches and a broader array of applications.
YARN Hadoop falls under the category of Cluster Management, which involves the management of resources across a cluster of machines. In the context of YARN Hadoop, it specifically focuses on efficiently allocating resources like CPU and memory to applications running on a Hadoop cluster. By providing a flexible and scalable framework for resource management, YARN allows organizations to run a wide range of distributed applications effectively.
Initially introduced in 2012 by Hortonworks, YARN Hadoop emerged as a significant advancement in the Hadoop ecosystem, addressing the limitations of the previous Hadoop MapReduce framework. The motivation behind creating YARN was to enhance the performance, scalability, and flexibility of Hadoop by decoupling resource management from job scheduling. This architectural shift laid the foundation for a more versatile and robust Hadoop framework, enabling businesses to process and analyze vast amounts of data more efficiently.
Currently, YARN Hadoop holds a considerable market share within the Cluster Management category, being widely adopted by organizations seeking to harness the power of distributed computing for big data processing. With the increasing demand for scalable and efficient data processing solutions, the market share of YARN Hadoop is expected to grow in the future. As businesses continue to leverage big data analytics for gaining insights and driving decision-making, technologies like YARN Hadoop will play a crucial role in enabling data-driven innovation and growth.
YARN Hadoop is a popular choice among companies for efficient cluster management, offering a plethora of benefits that enhance data processing capabilities and overall system performance.
YARN Hadoop provides dynamic resource allocation, allowing companies to efficiently manage and allocate resources based on application needs. This flexibility ensures optimal resource utilization, leading to improved system performance and reduced resource wastage compared to traditional static allocation systems.
YARN Hadoop offers unparalleled scalability, enabling companies to seamlessly scale their clusters up or down based on workload demands. Additionally, its fault-tolerant architecture ensures high system availability and reliability, minimizing disruptions and downtime compared to other cluster management technologies.
YARN Hadoop supports multi-tenancy, enabling companies to run multiple applications concurrently on the same cluster without interference. This feature ensures resource isolation and application stability, leading to enhanced operational efficiency and resource utilization compared to single-tenancy systems.
With YARN Hadoop, companies can leverage advanced job scheduling capabilities to prioritize and optimize job execution based on business priorities and resource availability. This proactive approach improves overall job throughput and reduces job latency compared to traditional batch processing systems.
YARN Hadoop seamlessly integrates with a wide range of Big Data tools and technologies, offering companies the flexibility to build diverse data processing pipelines. This interoperability simplifies system management and accelerates time-to-insight, setting YARN Hadoop apart from other cluster management solutions.
In conclusion, YARN Hadoop empowers companies with enhanced resource management, scalability, multi-tenancy support, advanced job scheduling, and seamless integration within the Big Data ecosystem, making it a robust choice for efficient cluster management needs.
YARN Hadoop is a widely used technology in the field of Cluster Management, with many prominent companies leveraging its capabilities for various purposes. Here are a few case studies highlighting the utilization of YARN Hadoop by leading companies:
Facebook: Facebook utilizes YARN Hadoop for efficient cluster management and resource allocation across its vast infrastructure. By deploying YARN, Facebook has been able to scale its data processing capabilities to handle the massive amounts of data generated by its users. The social media giant started using YARN Hadoop in 2012 and has since relied on it to power critical data processing pipelines.
LinkedIn: LinkedIn, the professional networking platform, implemented YARN Hadoop to optimize its data processing workflows and improve resource utilization. YARN's ability to manage resources dynamically and support a wide range of applications has enabled LinkedIn to efficiently process and analyze large volumes of user data. LinkedIn integrated YARN Hadoop into its infrastructure in 2013, leading to enhanced scalability and performance in its data processing operations.
Twitter: Twitter leverages YARN Hadoop for effective cluster resource management and job scheduling to support its real-time data processing needs. By adopting YARN, Twitter has achieved better resource utilization and improved job scheduling efficiency, enabling faster data processing and analysis. Twitter incorporated YARN Hadoop into its tech stack in 2014 and has since benefited from its robust cluster management capabilities.
These case studies showcase how companies like Facebook, LinkedIn, and Twitter have successfully implemented YARN Hadoop to streamline their data processing operations, enhance scalability, and improve resource utilization. By harnessing the power of YARN, these organizations have been able to effectively manage their cluster resources, optimize data processing workflows, and drive insights from large volumes of data.
您可以访问 TheirStack.com,获取使用 YARN Hadoop 的公司更新名单。我们的平台提供了一个全面的数据库,涵盖了使用各种技术和内部工具的公司。
截至目前,我们拥有关于 7 家使用 YARN Hadoop 的公司的数据。
YARN Hadoop 被广泛应用于包括 "Software Development", "Travel Arrangements", "It Services And It Consulting", "Advertising Services", "Software Development" 在内的各个行业的各种组织中。欲了解所有使用 YARN Hadoop 的行业的完整列表,请访问 TheirStack.com。
一些使用YARN Hadoop的公司包括Notion, Go2Andaman, Quental, Basis Technologies, Airbnb以及更多公司。您可以在TheirStack.com上找到使用YARN Hadoop的7家公司完整列表。
根据我们的数据,YARN Hadoop 在 美国 (3 companies), 印度 (1 companies), 西班牙 (1 companies) 最受欢迎。然而,它被全世界的公司所使用。
您可以在TheirStack.com上搜索YARN Hadoop,来找到使用该技术的公司。我们跟踪数百万家公司的招聘信息,并借此发现他们正在使用的技术和内部工具。