Hadoop 简明教程
Hadoop - Big Data Overview
由于新技术、设备以及像社交网站那样的通信手段的出现，人类制作的数据量每年都在快速增长。我们从时间开始到 2003 年产生数据的数量为 50 亿千兆字节。如果将数据以磁盘的形式堆叠起来，它可以填满整个足球场。同等数量的数据在 2011 中每两天产生一次，在 2013 中每十分钟产生一次。这个比率仍在剧烈增长。尽管产生的所有这些信息都是有意义的，并且在处理时可能是有用的，但它却被忽视了。
Due to the advent of new technologies, devices, and communication means like social networking sites, the amount of data produced by mankind is growing rapidly every year. The amount of data produced by us from the beginning of time till 2003 was 5 billion gigabytes. If you pile up the data in the form of disks it may fill an entire football field. The same amount was created in every two days in 2011, and in every ten minutes in 2013. This rate is still growing enormously. Though all this information produced is meaningful and can be useful when processed, it is being neglected.
What is Big Data?
Big data 是大量数据集的集合，无法使用传统计算技术进行处理。它不是单一技术或工具，而是一个完整的学科，其包括各种工具、技术和框架。
Big data is a collection of large datasets that cannot be processed using traditional computing techniques. It is not a single technique or a tool, rather it has become a complete subject, which involves various tools, technqiues and frameworks.
What Comes Under Big Data?
大数据涉及由不同设备和应用程序生成的数据。以下是属于大数据保护范围的一些领域。
Big data involves the data produced by different devices and applications. Given below are some of the fields that come under the umbrella of Big Data.
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Black Box Data − It is a component of helicopter, airplanes, and jets, etc. It captures voices of the flight crew, recordings of microphones and earphones, and the performance information of the aircraft.
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Social Media Data − Social media such as Facebook and Twitter hold information and the views posted by millions of people across the globe.
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Stock Exchange Data − The stock exchange data holds information about the ‘buy’ and ‘sell’ decisions made on a share of different companies made by the customers.
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Power Grid Data − The power grid data holds information consumed by a particular node with respect to a base station.
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Transport Data − Transport data includes model, capacity, distance and availability of a vehicle.
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Search Engine Data − Search engines retrieve lots of data from different databases.
因此，大数据包含海量、高速和可扩展性多种数据。其中数据有三种类型。
Thus Big Data includes huge volume, high velocity, and extensible variety of data. The data in it will be of three types.
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Structured data − Relational data.
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Semi Structured data − XML data.
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Unstructured data − Word, PDF, Text, Media Logs.
Benefits of Big Data
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Using the information kept in the social network like Facebook, the marketing agencies are learning about the response for their campaigns, promotions, and other advertising mediums.
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Using the information in the social media like preferences and product perception of their consumers, product companies and retail organizations are planning their production.
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Using the data regarding the previous medical history of patients, hospitals are providing better and quick service.
Big Data Technologies
大数据技术对于提供更准确的分析非常重要，这可能导致更具体决策，从而提高运营效率、降低成本，并降低业务风险。
Big data technologies are important in providing more accurate analysis, which may lead to more concrete decision-making resulting in greater operational efficiencies, cost reductions, and reduced risks for the business.
为了利用大数据的强大优势，你需要一个基础结构，该基础结构可以实时管理和处理海量的结构化和非结构化数据，并且可以保护数据隐私和安全性。
To harness the power of big data, you would require an infrastructure that can manage and process huge volumes of structured and unstructured data in realtime and can protect data privacy and security.
市场上有许多来自不同供应商（包括 Amazon、IBM、Microsoft 等）的各种技术来处理大数据。在研究用于处理大数据的技术时，我们会研究以下两类技术 −
There are various technologies in the market from different vendors including Amazon, IBM, Microsoft, etc., to handle big data. While looking into the technologies that handle big data, we examine the following two classes of technology −
Operational Big Data
这包括诸如 MongoDB 之类的系统，这些系统提供了可针对实际、交互式工作负载（其中数据会首先捕获和存储）的操作功能。
This include systems like MongoDB that provide operational capabilities for real-time, interactive workloads where data is primarily captured and stored.
NoSQL 大数据系统被设计用于利用在过去十年间出现的新的云计算架构，以实现经济高效地运行大规模计算。这让在操作大数据工作负载时更容易管理、成本更低、实施起来更快。
NoSQL Big Data systems are designed to take advantage of new cloud computing architectures that have emerged over the past decade to allow massive computations to be run inexpensively and efficiently. This makes operational big data workloads much easier to manage, cheaper, and faster to implement.
一些 NoSQL 系统能够基于实时数据以最少编码并无需数据科学家和额外基础结构来洞察模式和趋势。
Some NoSQL systems can provide insights into patterns and trends based on real-time data with minimal coding and without the need for data scientists and additional infrastructure.
Analytical Big Data
这些包括诸如海量并行处理 (MPP) 数据库系统和 MapReduce 之类的系统，这些系统为可能涉及大多数或所有数据的回顾和复杂分析提供了分析功能。
These includes systems like Massively Parallel Processing (MPP) database systems and MapReduce that provide analytical capabilities for retrospective and complex analysis that may touch most or all of the data.
MapReduce 提供了一种新型数据分析方法，它补充了 SQL 所提供的功能，并提供了基于 MapReduce 的系统，该系统可以从单台服务器扩展到数千台高端和低端机器。
MapReduce provides a new method of analyzing data that is complementary to the capabilities provided by SQL, and a system based on MapReduce that can be scaled up from single servers to thousands of high and low end machines.
这两类技术是互补的，经常一起部署。
These two classes of technology are complementary and frequently deployed together.
Operational vs. Analytical Systems
Operational |
Analytical |
|
Latency |
1 ms - 100 ms |
1 min - 100 min |
Concurrency |
1000 - 100,000 |
1 - 10 |
Access Pattern |
Writes and Reads |
Reads |
Queries |
Selective |
Unselective |
Data Scope |
Operational |
Retrospective |
End User |
Customer |
Data Scientist |
Technology |
NoSQL |
MapReduce, MPP Database |