简化数据架构并加强协作：Condé Nast 案例研究
技术
- 分析与建模 - 机器学习
- 应用基础设施与中间件 - 数据交换与集成
适用行业
- 建筑物
- 水泥
适用功能
- 销售与市场营销
- 仓库和库存管理
用例
- 拣选/分拣/定位
- 预测性维护
服务
- 数据科学服务
- 系统集成
关于客户
康泰纳仕是一家全球媒体公司，拥有《Vogue》、《GQ》、《连线》等颇具影响力的品牌。他们拥有大量的受众，包括数十亿的粉丝、社交追随者和每月的印刷读者。它们生成大量数据，这些数据存储在孤立的系统中。
挑战
全球媒体公司 Condé Nast 拥有复杂的数据架构，阻碍了数据团队之间的可扩展性和协作。他们将数据存储在孤立的系统中，并严重依赖数据工程师来完成简单的任务。
解决方案
Condé Nast 与 Databricks Lakehouse 一起实施了 dbt Cloud，以简化其数据架构并增强协作。他们使用 Databricks 构建可重用的数据摄取框架，并将其连接到 dbt 进行数据转换。新平台 Evergreen 为所有数据消费者提供了对分析就绪和业务就绪数据集的访问。
运营影响
数量效益
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