数据仓库中的敏捷开发

Nayem Rahman, Dale Rutz, Shameem Akhter
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引用次数: 25

摘要

传统的数据仓库项目遵循瀑布式开发模型,其中项目经历不同的阶段,如需求收集、设计、开发、测试、部署和稳定。然而,业务需求和技术本质上都是复杂的,瀑布模型可能需要6到9个月才能完全实现一个解决方案;到那时,商业和技术往往已经发生了很大的变化。其结果是令涉众失望和开发团队沮丧。敏捷开发以迭代的方式实现项目。敏捷方法也被称为60%解决方案,它寻求在初始版本中交付超过一半的用户需求,并在定期计划的一系列后续版本中进行改进。敏捷数据仓库方法大大增加了按时在预算范围内成功实现的可能性。本文讨论了数据仓库和商业智能中的敏捷开发方法、敏捷方法的含义、在商业智能(BI)需求频繁变化的情况下管理数据仓库中的更改,并演示了敏捷性对业务的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Agile Development in Data Warehousing
Traditional data warehouse projects follow a waterfall development model in which the project goes through distinct phases such as requirements gathering, design, development, testing, deployment, and stabilization. However, both business requirements and technology are complex in nature and the waterfall model can take six to nine months to fully implement a solution; by then business as well as technology has often changed considerably. The result is disappointed stakeholders and frustrated development teams. Agile development implements projects in an iterative fashion. Also known as the sixty percent solution, the agile approach seeks to deliver more than half of the user requirements in the initial release, with refinements coming in a series of subsequent releases which are scheduled at regular intervals. An agile data warehousing approach greatly increases the likelihood of successful implementation on time and within budget. This article discusses agile development methodologies in data warehousing and business intelligence, implications of the agile methodology, managing changes in data warehouses given frequent change in business intelligence (BI) requirements, and demonstrates the impact of agility on the business.
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