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<a class="post-title-link" href="/article/data-warehouse-practice-for-major-companies/" itemprop="url">
各大公司的数据仓库落地方案
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<p>前面有几篇文章讲了数据仓库建设的方法和思路，覆盖了数据仓库设计过程中的多数要素。到了具体的落地实践方面，因为具体业务、数据使用场景的要求以及人力物力等因素的影响，实现的方案非常多样。如今大数据分布式存储引擎和计算引擎有多种成熟的选择，相互之间各有优势，各家公司在实际使用过程中也都会各有偏好。我选取了一些我通过在各种论坛活动上了解到的，以及在网上或者书上看到的多家互联网公司的数据仓库落地方案，分类梳理了一下，希望能给数据仓库设计者包括自己一些新的思路。</p>
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kimball维度建模的维度表技术
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<time title="Created: 2018-09-13 19:01:25" itemprop="dateCreated datePublished" datetime="2018-09-13T19:01:25+08:00">2018-09-13</time>
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<p>在维度建模过程中，维度表的设计和生成是非常重要的一环。维度表中的内容反映的是事实发生的环境，对数据仓库系统可解释性和可用性起到了很重要的作用。可以说，维度表设计的好坏很大程度上决定了数据仓库的成败。在维度表的设计和字段的解释说明上，是值得投入大量的精力的。</p>
<p>在实际的设计过程中，需要考虑的问题有很多，这篇文章提的三点在工作中比较常见和重要的。</p>
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数据仓库分层和事实表设计
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<time title="Created: 2018-09-12 10:57:38" itemprop="dateCreated datePublished" datetime="2018-09-12T10:57:38+08:00">2018-09-12</time>
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<p>数据仓库的分层是在搭建数据仓库的时候最先考虑的问题之一，决定了后面的落地步骤是否顺利。之前我们在用金矿到黄金首饰的制作过程类比数据仓库的时候，就提到了数据在处理的各个步骤，这些步骤反映在数据仓库上就是分层设计。需要注意的是，数据仓库的分层是一个偏向工程化的概念，在不同的公司、不同业务、不同数据场景下，最优的分层设计可能会有差异。这篇文章后面讲到的只是对在工作中实际设计情况和了解到业内有限数量的处理方案的一个融合。</p>
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kimball的维度建模
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<p>数据仓库的概念已经提出很多年，在之前还有一个“决策支撑系统”的叫法。在这漫长的岁月里，工程师和学者提出了很多的方法论和技巧。到了现在，大数据的技术栈到了以hadoop为主的年代，kimball的维度建模理论是被接受和使用最广泛的方法论。</p>
<p>维度建模的概念并不是kimball的原创，是由他带到了数据仓库的建设理论之中。所以这整套理论的核心，也不仅仅是维度建模的思想，还有附带的一系列原则、方法和技巧。当然还需要注意点是，这些原则和方法大多是从实践中总结出来的。我们的主流技术栈已经不同，这些经典的内容也已经不能生硬的照搬，要根据自己的技术栈和业务特点做一些适应性的变化。不过，这并影响kimball的维度建模给我们当前的数据仓库建设工作起到了很大的参考和引导作用。</p>
<p>这篇文章希望通过几个维度建模的核心词汇为引导，介绍维度建模对我们最有用的内容。</p>
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我们需要什么样的数据仓库
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<p>时至今日，我们都已经意识到大数据时代的来临。数据对公司的重要性得到普遍的认同。这些年来，各个行业的公司，包括互联网、金融、汽车、贸易等，内部都设立了专门的大数据部门，地位也越来越重要，甚至专门的大数据公司也遍地开花。不管在什么行业，大家都希望能够通过获得数据，了解自己的用户以及用户对自己产品的评价。</p>
<p>我们知道了数据是有价值的，那数据价格该如何体现。在这篇文章中，答案是通过数据仓库。现在，即使是刚刚接触大数据的同学们，也都知道了数据仓库这个名字，并且对数据仓库的作用有一定的认识。可能会把数据仓库的存在的当成是理所当然的事情。而这篇文章的目的，就是希望去重新思考这个问题，让我们理解为什么需要数据仓库，并且试图说明白如何评价一个数据仓库是不是好的、有效的。</p>
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