Snowflake 使用 Fivetran 进行综合数据堆栈开发
技术
- 分析与建模 - 机器学习
- 传感器 - 气流传感器
适用行业
- 设备与机械
适用功能
- 销售与市场营销
用例
- 时间敏感网络
服务
- 云规划/设计/实施服务
- 数据科学服务
关于客户
Snowflake 成立于 2012 年，随着现代数据堆栈生态系统的不断发展，已变得无处不在，为全球 6,000 多家客户提供对数据云的访问。 Snowflake 自身的增长是由快速迭代和一流的数据实践推动的，包括开发强大的内部数据堆栈，使组织中的每个员工都可以访问数据。这一迅速崛起的核心是 Snowflake 的营销情报功能，这是一个拥有大胆愿景的团队：预测实时投资回报率，动态优化所有 Snowflake 营销计划，颠覆传统的 B2B 营销分析实践，并创造巨大的效率。
挑战
Snowflake 是一家领先的数据云公司，希望将其数据集中在该组织的 Snowflake 实例“Snowhouse”中，以支持细分模型、推荐引擎，并最终构建 360 度客户视图。 Snowflake 的营销情报团队有一个大胆的愿景，即预测实时投资回报率，以动态优化所有 Snowflake 营销计划，颠覆传统的 B2B 营销分析实践，并创造巨大的效率。然而，该公司在打破数据孤岛和实现高效分析方面面临挑战。 Snowflake 过去将其数据建模和转换逻辑保存在单独的 BI 工具中，这既耗时又容易出错。每当企业需要从工具中运行模型或进行临时分析时，分析师都需要从头开始重新创建模型。
解决方案
Snowflake 与 Fivetran 合作构建了全面的数据堆栈。 Fivetran 是 Snowflake 现代数据堆栈的关键部分，提供了 900 多个连接器，通过 Google、Bing 和 Facebook Ads 等营销分析连接器以及 Marketo、Salesforce 和 Jira 等核心 SaaS 工具，移动超过 4 亿个月活跃行。 dbt Core™ 的实施为企业内的最终用户提供了更加灵活的体验。该团队看到了更好的整体性能，因为大多数计算密集型计算都是在此过程的早期进行的。 Snowflake 还使用 Airflow 来管理 dbt™ 作业，并定义他们想要更新模型和表格的频率，使分析师能够在数据刷新时间与性能和成本考虑之间取得平衡。该公司建立了一个数据消费层，营销分析团队可以在其中进行协作并邀请其业务合作伙伴开始与数据进行交互。
运营影响
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