Databricks 从数据孤岛过渡到统一数据湖屋
技术
- 分析与建模 - 机器学习
- 分析与建模 - 预测分析
适用行业
- 教育
- 设备与机械
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
- 时间敏感网络
服务
- 数据科学服务
关于客户
Databricks 是一家以帮助数据团队解决世界上最大的挑战而自豪的企业。它由 Apache Spark、Delta Lake 和 MLflow 的原始创建者于 2013 年创立。 Databricks 基于云中的现代 Lakehouse 架构构建，结合了最好的数据仓库和数据湖，为数据和人工智能提供开放且统一的平台。该公司迅速扩张，导致需要集中和记录的数据。然而，数据孤岛出现在企业周围，包括营销团队，数据存储在自己的数据仓库中。
挑战
Chris Klaczynski 是 Databricks 的营销分析经理，其任务是支持推动管道生成、扩大数据库和提高投资回报率等主要营销目标。然而，随着 Databricks 的迅速扩张，对集中化和记录数据的需求变得越来越明显。数据孤岛出现在企业周围，包括克里斯的营销团队，数据存储在自己的数据仓库中。为克里斯新建的仪表板提供可靠、及时的数据对于营销运营保持平稳运行至关重要。然而，如果没有专门的工程资源，并且面对迅速扩大的营销团队，根据需求进行扩展几乎是不可能的。 Databricks 的传统数据仓库面临着许多挑战，包括 Salesforce 和 Marketo 管道的问题、将数据本地附加到现有表的问题，以及总是破坏管道的架构更改，导致中断和陈旧、不可信的数据。
解决方案
Chris 开始寻找一种低代码、交钥匙解决方案，为他的团队提供所需的可靠管道。他决定了一个三点战略：走出数据工程“丛林”，进入洞察和预测分析领域，聘请分析师和数字中小企业，而不是数据工程师和 DevOps，实现数据管道自给自足，并利用 Delta 和 AutoML 。 Fivetran 作为满足 Chris 数据管道需求的解决方案立即脱颖而出。在试用该产品时，设置快速而简单。 Chris 的团队现在使用 Fivetran 从所有核心营销源系统引入数据：Marketo、Salesforce、Facebook Ads 和 Google Analytics。到达湖屋后，Chris 的团队将数据与产品和中心团队的其他来源结合起来，以有意义的方式对其进行转换，以运行数据科学、机器学习并生成重要的 Tableau 仪表板以进行分析。
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