September 2013
Intermediate to advanced
264 pages
5h
Chinese
Content preview from Bad Data 技術手冊
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第十九章：揭開資料品質分析的神祕面紗：瞭解何時你的資料已經夠好了
正確（Correct）
在確認過你的資料是完整且前後連貫的之後，你尚未準備好要產生一些數字。現在你必
須要問你自己，你的資料是否足夠
正確
，讓你能夠做你嘗試要做的事情。在分析之前先
考量這件事看起來有點奇怪，因為執行分析時，通常某種程度的也會驗證資料集合；但
記住，在你移動到主要事件上面之前，也有正確性的「子維度」要被重複驗證。跟測試
前後連貫性相似，正確性需要某種程度的領域知識。
其中一件要記得的事情，就是正確性本身能夠是相關的。試想你從一個由上百台伺服器
所組成的分散式系統中取得資料，而你希望測量整個系統的訊息流程中元件服務之間的
延遲。你能夠直接假設所有機器的時間都是同步的嗎，或是假設你的歷史記錄訊息的記
錄時間戳記都是同步的？也許。但就算是你自己配置這個系統，事情還是會改變（並且
壞掉）。
其中一個簡單的確認，就是要確認時間戳記是向適當的方向移動。假設系統的訊息流程
分別是依照
s1
、
s2
以及
s3
的順序。你可以確認時間戳記是如下般相關：
message_timestamp(s1) <= message_timestamp(s2) <= message_timestamp(s3)
若通過
s2
訊息的時間戳記，是一致的小於（較新）當這些相同的訊息經過
s1
時的時間
戳記。只要你認為時間旅行是不可能的，你會推論出，你在資料中擁有系統性的錯誤
（舉例來說，有可能是因為在
s1
或在
s2
上錯誤的配置時鐘）。你可以修正系統的時鐘
並且重新建立資料集合，或是調整儲存好的時間戳記 ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.