hadoop和teradata有什么区别

问题描述

我碰了一个Teradata。我从来没有触摸过hadoop，但从昨天开始，我正在做一些研究。通过对两者的描述，它们似乎是可互换的，但在一些论文中，它被写为它们用于不同的目的。但是我发现的都是模糊的。我很困惑。

I've touched a Teradata. I've never touched hadoop, but since yesterday, I am doing some research on that. By description of both, they seem quite interchangable, but in some papers it is written that they serve for different purposes. But all I found is vague. I am confused.

有没有人有这两个人的经验？它们之间的严重差别是什么？

Has anybody experience with both of them? What is the serious difference between them?

简单示例：我想构建ETL，它将转换数十亿行原始数据并将它们组织到DWH。然后做一些资源昂贵的分析。为什么使用TD？为什么选择Hadoop？或为什么不？

Simple Example: I want to build ETL which will transform billions rows of raw data and organize them to DWH. Then do some resources expensive analysis on them. Why use TD? Why Hadoop? or why not?

推荐答案

我认为这篇文章的标题为MapReduce和并行DBMSs：朋友或者敌人在描述每种技术最好的情况下是一个很好的工作。简而言之，Hadoop非常适合存储非结构化数据和运行并行转换以清理传入数据，其中DBMS能够快速执行复杂查询。

I think this article titled 'MapReduce and Parallel DBMSs: Friends or Foes' does quite a good job describing the situations where each technology works best. In a nutshell, Hadoop is excellent for storing unstructured data and running parallel transformations to 'sanitize' incoming data, where DBMSs excel at executing complex queries quickly.

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