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Cloudera
概述
公司介绍
Cloudera 为任何地方的任何数据提供企业数据云,从边缘到人工智能。Cloudera 于 2008 年由硅谷领先公司的一些最聪明的人创立,包括谷歌 (Christophe Bisciglia)、雅虎! (Amr Awadallah)、甲骨文 (Mike Olson) 和 Facebook (Jeff Hammerbacher)。 Hadoop 的共同创建者 Doug Cutting 于 2009 年加入公司,担任首席架构师,并继续担任该职务。如今,Cloudera 拥有 1,600 多名员工。他们在全球 24 个国家设有办事处,总部位于加利福尼亚州帕洛阿尔托。
物联网解决方案
Cloudera 数据平台是业界首个企业数据云 CDP,可管理和保护所有主要公共云和私有云中的数据生命周期——将本地环境无缝连接到公共云以提供混合体验。由于 CDP 具有以下能力,IT 可以提供业务所需的速度和敏捷性: - 通过自动扩展、暂停和恢复来控制云成本 - 基于分析和机器学习优化工作负载 - 查看任何云和瞬态集群中的数据沿袭 - 使用跨混合云和多云的单一管理平台 - 扩展到 PB 级数据和数千个不同的用户
子公司
物联网应用简介
技术栈
Cloudera的技术栈描绘了Cloudera在平台即服务 (paas), 应用基础设施与中间件, 分析与建模, 和 网络安全和隐私等物联网技术方面的实践。
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设备层
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边缘层
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云层
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应用层
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配套技术
技术能力:
无
弱
中等
强
实例探究.
Case Study
Direct Marketing Solution
Marketing to prospects (direct mail, telemarketing, and email) is expensive. Targeting incorrectly can hurt your brand, leaving prospects feeling spammed. Traditional techniques are not very sophisticated, resulting in low response rates which in turn leads to high cost-per-lead/acquisition numbers.
Case Study
Numberly Leverages Cloudera Machine Learning Platform for Real-Time CRM Optimization
In the data-driven world, marketing technology companies like Numberly are increasingly dependent on their ability to deliver valuable insights from diverse, large datasets to maintain a competitive edge. The greatest return on marketing and advertising spend is achieved by delivering precisely targeted, relevant, and timely messages. To maintain its position as a global leader, Numberly faced the challenge of including, processing, and reconciling large and diverse datasets, such as static data from CRMs and streaming sensor data. They needed to make this data accessible for data science and analytics in near real-time.
Case Study
Global IT Company Enhances Search Capabilities with Modern Data Platform
The global information technology services company, which provides one of the largest e-commerce platforms in the world, was facing challenges due to the exponential growth in data volume resulting from increased internet transactions. The company needed to improve the relevance of information for discovery and required a semantic search engine to power the search function for all applications on its platform. This was crucial to understand user intent through search context and improve the relevance of results. The company also needed to create a modern architecture framework to enable better searches, replicate data, and perform experimental customization analytics. The challenge was to move data efficiently to allow effective searchability, a task the company was struggling with. The platform also needed to handle increased search traffic and data volumes expected in the future, while adhering to high security and compliance standards and avoiding high costs.
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