Google Cloud Platform > 实例探究 > Apester:使用 Google Cloud 进行扩展和节省

Apester:使用 Google Cloud 进行扩展和节省

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技术
  • 分析与建模 - 机器学习
  • 平台即服务 (PaaS) - 应用开发平台
适用行业
  • 水泥
  • 建筑与基础设施
适用功能
  • 维护
  • 仓库和库存管理
用例
  • 施工管理
  • 时间敏感网络
服务
  • 云规划/设计/实施服务
  • 系统集成
关于客户
Apester 是一家总部位于纽约和特拉维夫的公司,帮助出版商、广告商和企业讲述引人入胜的在线故事。它提供了用于创建、分发交互式视觉内容体验并从中获利的工具。自迁移到 Google Cloud 以来,Apester 的客户群规模增加了两倍。
挑战
自 2014 年推出以来,Apester 的受欢迎程度呈爆炸式增长,每月吸引约 1 亿独立用户。然而,其现有的商业智能和数据仓库系统显示出分析能力紧张和限制的迹象。 Apester 希望利用其不断增长的客户群并获得更多见解,而不必担心规模或许可费用。
解决方案
Apester 向 Google Cloud 寻求可扩展且开源兼容的解决方案。它围绕 Cloud Dataflow、Cloud Dataproc 和 Cloud Bigtable 以及用于数据处理和分析的开源 Apache Beam 构建了数据解决方案。随着时间的推移,Apester 完全迁移到 Google Cloud,使用 Kubernetes Engine 作为其基础设施的骨干,使用 Cloud Pub/Sub 作为消息总线。它还利用 Stackdriver 进行日志记录和监控,并利用 Cloud IAM 进行轻松的权限管理。
运营影响
  • The shift to Google Cloud has not only saved Apester time and money, but it has also changed the way the company thinks about its processes. The IT team has developed a service-based mindset that focuses on results and improving the overall Apester product line, rather than fussing over the minutiae of DevOps. With the migration done, Apester continues to look for new ways to improve and evolve its products. The company is using Cloud Natural Language APIs to enhance the personalization of its service. Along with the data held in BigQuery, Apester’s work with the Cloud Natural Language modules provides the basis for an exploration into machine learning (ML). The company is heavily investing in its ML capabilities, and started using Tensorflow for its pipeline, enabling Apester to become even more responsive to its customers’ needs even as its audience expands.
数量效益
  • Delivered 3.5 billion story experiences a year for half the cost of the previous infrastructure
  • Cut deployment time from four hours to less than a minute with Kubernetes
  • Tripled its user base while halving infrastructure costs

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