使用Dask的公司
A flexible library for parallel computing in Python
使用Dask的公司列表
curl --request POST \
--url "https://api.theirstack.com/v1/companies/search" \
--header "Accept: application/json" \
--header "Content-Type: application/json" \
--header "Authorization: Bearer <api_key>" \
-d "{
\"company_technology_slug_or\": [
\"dask\"
]
}"Technology
is any of
Dask
员工数61kFinancial ServicesDask高置信度
员工数1.9k收入$1.6BComputers and Electronics ManufacturingDask高置信度
员工数273Software DevelopmentDask高置信度
员工数710收入$66MSoftware DevelopmentDask高置信度
员工数3.5k收入$12MSoftware DevelopmentDask高置信度
员工数6.2k收入$764MSoftware DevelopmentDask高置信度
员工数437收入$10MSoftware DevelopmentDask高置信度- 员工数80Software DevelopmentDask高置信度
员工数5k收入$801MEngineering ServicesDask高置信度
员工数10k收入$2.7BBiotechnology ResearchDask高置信度
我们拥有关于3,748家公司和用户使用Dask的数据。我们的Dask用户和客户列表可供下载，并附有重要的公司信息，包括行业分类、组织规模、地理位置、融资轮次和收入数据等。
技术使用统计和市场份额
如何定位 Dask 用户
- 如何自定义此列表？
您可以根据地理位置、行业、公司规模、收入、技术使用、职位发布等需求自定义此数据。您可以将数据下载为Excel或CSV格式。
- 如何在公司采用此技术时收到通知？
您可以为此数据设置提醒。您可以通过选择您感兴趣的技术开始，当有新公司使用该技术时，您将收到收件箱中的提醒。
- 如何将此数据导入我的CRM？
您可以将此数据导出为Excel文件，然后导入到您的CRM中。您也可以将数据导出到API。
常见问题
我们的数据来源于从数百万家公司收集的职位发布。我们监控公司网站、招聘网站和其他招聘平台上的这些发布。分析职位发布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。
我们每天刷新数据，以确保您访问的是最新的信息。这一频繁的更新过程保证了我们的洞察和情报反映行业的最新发展和趋势。
Dask is a flexible parallel computing library in Python that enables seamless execution of complex computations. It provides advanced parallel and distributed computing capabilities to tackle tasks that involve large datasets, thus making it an essential tool for data scientists and engineers. Dask allows users to scale their data-intensive applications from a single machine to a cluster of machines, effectively managing the distribution of workloads and optimizing performance.
In the realm of Data Science Tools, Dask falls under the category of distributed computing frameworks. Its primary focus is on enabling efficient parallel processing of data, making it particularly valuable for tasks like data manipulation, machine learning model training, and large-scale data analysis. By leveraging Dask, users can overcome the limitations of traditional single-node computing and explore new possibilities in handling big data workloads with ease.
Founded in 2015 by the team at Anaconda, Dask originated from the need to address the challenges posed by the growing demand for scalable data processing in the Python ecosystem. Motivated by the desire to provide a flexible and user-friendly solution for parallel computing, the creators of Dask set out to develop a tool that could seamlessly integrate with existing Python data libraries while offering enhanced performance and scalability.
Currently, Dask maintains a strong foothold in the data science and scientific computing domains, attracting a growing user base due to its versatility and efficiency in handling large-scale computational tasks. With an increasing demand for scalable data processing solutions, Dask's market share within the Data Science Tools category is expected to experience significant growth in the coming years. As organizations continue to grapple with ever-expanding datasets and the need for faster processing speeds, Dask's role in enabling efficient parallel computing is likely to become even more pronounced, solidifying its position as a leading technology in the field.