3.HBase In Action 第一章-HBase简介（1.1.1 大数据你好呀）
Let’s take a closer look at the term Big Data. To be honest, it’s become something of a loaded term, especially now that enterprise marketing engines have gotten hold of it. We’ll keep this discussion as grounded as possible.
让我们仔细思考下“大数据”这个词。老实，它已经成为当下的一种流行说法，特别是现在企业营销方面已经紧紧地抓住了它来推广产品。我们会尽可能地继续这个讨论。
What is Big Data? Several definitions are floating around, and we don’t believe that any of them explains the term clearly. Some definitions say that Big Data means the data is large enough that you have to think about it in order to gain insights from it. Others say it’s Big Data when it stops fitting on a single machine. These definitions are accurate in their own respect but not necessarily complete. Big Data, in our opinion, is a fundamentally different way of thinking about data and how it’s used to drive business value. Traditionally, there were transaction recording (OLTP) and analytics (OLAP) on the recorded data. But not much was done to understand the reasons behind the transactions or what factors contributed to business taking place the way it did, or to come up with insights that could drive the customer’s behavior directly. In the context of the earlier LinkedIn example, this could translate into finding missing connections based on user attributes, second-degree connections, and browsing behavior, and then prompting users to connect with people they may know. Effectively pursuing such initiatives typically requires working with a large amount of varied data.
大数据是什么?有好几个定义在那漂着呢，呵呵。我们不相信存在能清楚地解释它的任何术语。一些定义说，大数据意味着数据足够大，大到你觉得有必要好好地参考下它，以便从它获得一些见解。还有一些定义说，当一个数据无法用一台计算机装下的时候，就是大数据。这些定义有它自己的道理，但不一定是完全准确的。大数据，在我们看来，它是一种对数据和数据如何驱动业务价值的全新的思维方式。传统上, 我们有交易记录(OLTP)和交易记录的分析(OLAP)行为。但没有多少行为是为了理解交易背后的原因，影响业务发生方式的因素，或者提出可以直接驱动客户行为的见解。在早些时候LinkedIn的例子中，系统基于用户的属性，用户的二度人脉和用户的浏览行为等，发现并提示用户联系他们可能认识的人。想有效地实现这些功能效果通常需要处理大量不同的数据。
This new approach to data was pioneered by web companies like Google and Amazon, followed by Yahoo! and Facebook. These companies also wanted to work with different kinds of data, and it was often unstructured or semistructured (such as logs of users’ interactions with the website). This required the system to process several orders of magnitude more data. Traditional relational databases were able to scale up to a great extent for some use cases, but doing so often meant expensive licensing and/or complex application logic. But owing to the data models they provided, they didn’t do a good job of working with evolving datasets that didn’t adhere to the schemas defined up front. There was a need for systems that could work with different kinds of data formats and sources without requiring strict schema definitions up front, and do it at scale. The requirements were different enough that going back to the drawing board made sense to some of the internet pioneers, and that’s what they did. This was the dawn of the world of Big Data systems and NoSQL. (Some might argue that it happened much later, but that’s not the point. This did mark the beginning of a different way of thinking about data.)
这种针对数据的新方法是由网络公司首创的，一开始是谷歌和亚马逊，紧随其后的是雅虎和Facebook。这些公司还想处理不同类型的数据,而且这些数据经常是非结构化或半结构化的(比如用户与网站的交互日志)。这需要系统处理多好几个数量级的数据。传统的关系数据库能够通过扩展在很大程度满足一些应用系统的需求，但是这样做往往意味着昂贵的许可费用和（或）复杂的应用程序逻辑。同时由于他们需要使用数据模型，而数据集并不遵循预先定义的模式，所以他们并不能很好地处理不断发展变化的数据集。于是我们需要一种应用系统，能够处理不同类型的数据格式，数据来源不需要严格的模型定义，并且还能做大规模的服务群集。需求是各不相同的，所以回到白板时期对一些互联网先驱来讲是有意义的，而且他们正在这么做。现在正是NoSQL和大数据系统的黎明期。(有些人可能会认为它的发生在太晚了,但这不是重点。它开启了一种不同的思考数据的方式。)
As part of this innovation in data management systems, several new technologies were built. Each solved different use cases and had a different set of design assumptions and features. They had different data models, too.
作为数据管理系统创新的一部分，目前业界已经出现了一些新的技术。每种技术都是为了解决不同的问题和拥有一些不同的设计理念和特性的，同时也有着不同的数据模型。
How did we get to HBase? What fueled the creation of such a system? That’s up next.
我们应该如何开启HBase的学习? 是什么原因推动人们去创建了这样一个数据系统? 这是我们的下一个话题，敬请期待。
