使用Google BigQuery的公司
Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure. Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.
使用Google BigQuery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\": [
\"google-bigquery\"
]
}"Technology
is any of
Google BigQuery
员工数3.2k收入$440MIT Services and IT ConsultingGoogle BigQuery高置信度
员工数491k收入$157BRetailGoogle BigQuery高置信度
员工数14k收入$12BRetailGoogle BigQuery高置信度
员工数24k收入$6.2BComputer and Network SecurityGoogle BigQuery高置信度
员工数738k收入$63BBusiness Consulting and ServicesGoogle BigQuery高置信度
员工数186k收入$170BMotor Vehicle ManufacturingGoogle BigQuery高置信度
员工数96Advertising ServicesGoogle BigQuery高置信度
员工数85k收入$29BFinancial ServicesGoogle BigQuery高置信度
员工数199kFinancial ServicesGoogle BigQuery高置信度
员工数19k收入$5.1BFinancial ServicesGoogle BigQuery高置信度
我们拥有关于18,490家公司和用户使用Google BigQuery的数据。我们的Google BigQuery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 Google BigQuery 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。
Google BigQuery is a renowned cloud-based data warehouse provided by Google Cloud Platform, allowing users to analyze massive datasets quickly using SQL queries. By employing the power of Google's infrastructure, BigQuery can efficiently process petabytes of data without requiring upfront hardware investment. This serverless, highly scalable solution enables businesses to derive valuable insights from their data in real-time, facilitating data-driven decision-making.
BigQuery falls under the category of Big Data as a Service, offering a cloud-based model for handling and analyzing large volumes of data efficiently. It eliminates the need for on-premises infrastructure to manage and process extensive datasets, making it a cost-effective and flexible solution for enterprises seeking to harness the power of big data analytics. BigQuery's ease of use, scalability, and integration capabilities with other Google Cloud services position it as a leading choice for businesses aiming to leverage big data effectively.
Google BigQuery was founded in 2010 by Google as a response to the growing demand for scalable data processing and analytics solutions. The motivation behind creating BigQuery was to provide businesses with a fast and cost-effective way to analyze vast amounts of data stored in the cloud. Over the years, Google has continuously enhanced BigQuery by adding new features and integrations, solidifying its position as a top-tier data warehouse solution in the market.
In the current market scenario, Google BigQuery holds a significant market share within the Big Data as a Service category. With the increasing adoption of cloud-based data analytics solutions, BigQuery is poised for further growth in the future. As businesses continue to prioritize data-driven decision-making and the demand for scalable data processing platforms rises, Google BigQuery is likely to maintain its market dominance and expand its user base across various industries.