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首页《Hadoop启蒙指南》:轻松探索大数据世界
《Hadoop Illuminated》是一本旨在为大数据和Hadoop领域提供轻松入门指南的书籍,特别强调无需深入的技术知识也能理解和学习。作者Mark Kerzner和Sujee Maniyam以一种易于理解的方式,将复杂的技术内容分解,使读者能够轻松上手Hadoop生态系统。本书的目标是帮助读者克服Hadoop和大数据技术带来的挑战,同时激发他们对这个领域的兴趣。 书中涵盖了Hadoop的基本概念,包括其分布式计算模型、HDFS(Hadoop分布式文件系统)和MapReduce编程模型,这些都是大数据处理的核心组件。对于那些对Apache Hadoop项目及其生态不了解的读者,这本书提供了清晰的介绍,帮助他们建立起扎实的基础。通过实例和实用的案例,读者可以逐步掌握如何在实际场景中部署、管理和优化Hadoop集群。 除了理论内容,书中还配以GitHub上的开源项目链接,如Hadoop Book和HI-labs,以便读者参与到实际的开发实践和实验中,进一步提升技能。此外,作者鼓励读者在遇到问题时积极参与社区交流,分享反馈,形成一个互助学习的氛围。 《Hadoop Illuminated》的出版得到了编辑、同事和众多贡献者的支持,他们共同走过了建立和维护工作集群的艰辛之路,希望能减轻后来者的负担。书中的观点并不一定代表作者个人立场,而是体现了开源社区的精神,强调了合作与分享的重要性。 版权方面,本书由Hadoop Illuminated LLC于2013年发布,保留所有权利。这是一本既实用又寓教于乐的资源,适合初学者、数据工程师以及对大数据技术感兴趣的任何人阅读,是探索和掌握Hadoop世界的理想起点。
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About Authors
3
Mark contributes to a number of Hadoop-based projects, and his open source projects can be found
on GitHub [http://github.com/markkerzner]. He writes about Hadoop and other technologies in his blog
[http://shmsoft.blogspot.com/].
Mark does Hadoop training for individuals and corporations; his classes are hands-on and draw heavily
on his industry experience.
Links:
LinkedIn [https://www.linkedin.com/in/markkerzner] || GitHub [https://github.com/markkerzner] || Per-
sonal blog [http://shmsoft.blogspot.com/] || Twitter [https://twitter.com/markkerzner]
Sujee Maniyam (Author)
Sujee Maniyam is an experienced/hands-on Big Data architect. He has been developing software for the
past 12 years in a variety of technologies (enterprise, web and mobile). He currently focuses on Hadoop,
Big Data and NoSQL, and Amazon Cloud Services. His clients include early stage startups and enterprise
companies.
Sujee stays active in the Hadoop / Open Source community. He runs a developer focused Meetup called
'Big Data Gurus' [http://www.meetup.com/BigDataGurus/]. He has also presented at a variety of Meetups
Sujee contributes to Hadoop projects and his open source projects can be found on GitHub. He writes
about Hadoop and other technologies on his website.
Sujee does Hadoop training for individuals and corporations; his classes are hands-on and draw heavily
on his industry experience
Links:
LinkedIn [http://www.linkedin.com/in/sujeemaniyam] || GitHub [https://github.com/sujee] || Tech writ-
ings [http://sujee.net/tech/articles/] || Tech talks [http://sujee.net/tech/talks/] || BigDataGuru meetup [http://
www.meetup.com/BigDataGurus/]
Rebecca Kerzner (Illustrator)
The delightful artwork appearing in this book was created by Rebecca Kerzner. She is seventeen and stud-
ies at Beren Academy in Houston, TX. She has attended the Glassell school of art for many summers.
Her collection of artwork can be seen here [https://picasaweb.google.com/110574221375288281850/Re-
beccaSArt].
Rebecca started working on Hadoop illustrations two years ago, when she was fifteen.
It is interesting to follow her artistic progress. For example, the first version of the
"Hadoop Zoo" can be found here [https://plus.google.com/photos/110574221375288281850/al-
bums/5850230050814801649/5852442815905345938?banner=pwa]. Note the programmer's kids who
"came to see the Hadoop Zoo" are all pretty young. But as the artist grew, so also did her heroes, and
in the current version of the same sketch [https://plus.google.com/photos/110574221375288281850/al-
bums/5850230050814801649/5852442453552955282?banner=pwa] you can see the same characters, but
older, perhaps around seventeen years of age. Even the Hadoop elephant appears more mature and serious.
All of Rebecca's Hadoop artwork can be viewed here [https://plus.google.com/pho-
tos/110574221375288281850/albums/5850230050814801649?banner=pwa].
![](https://csdnimg.cn/release/download_crawler_static/9429620/bgd.jpg)
About Authors
4
Contributors
We would like to thank the following people that helped us make this book a reality.
• Ben Burford : github.com/benburford [https://github.com/benburford]
![](https://csdnimg.cn/release/download_crawler_static/9429620/bge.jpg)
5
Chapter 3. Why do I Need Hadoop ?
Most people will consider hadoop because they have to deal with Big Data. See Chapter 4, Big Da-
ta [7] for more.
Figure 3.1. Too Much Data
3.1. Hadoop provides storage for Big Data at
reasonable cost
Storing Big Data using traditional storage can be expensive. Hadoop is built around commodity hardware.
Hence it can provide fairly large storage for a reasonable cost. Hadoop has been used in the field at Peta
byte scale.
One study by Cloudera suggested that Enterprises usually spend around $25,000 to $50,000 dollars per
tera byte per year. With Hadoop this cost drops to few thousands of dollars per tera byte per year. And
hardware gets cheaper and cheaper this cost continues to drop.
More info : Chapter 8, Hadoop Distributed File System (HDFS) -- Introduction [25]
![](https://csdnimg.cn/release/download_crawler_static/9429620/bgf.jpg)
Why do I Need Hadoop ?
6
3.2. Hadoop allows to capture new or more da-
ta
Some times organizations don't capture a type of data, because it was too cost prohibitive to store it. Since
Hadoop provides storage at reasonable cost, this type of data can be captured and stored.
One example would be web site click logs. Because the volume of these logs can be very high, not many
organizations captured these. Now with Hadoop it is possible to capture and store the logs
3.3. With Hadoop, you can store data longer
To manage the volume of data stored, companies periodically purge older data. For example only logs
for the last 3 months could be stored and older logs were deleted. With Hadoop it is possible to store the
historical data longer. This allows new analytics to be done on older historical data.
For example, take click logs from a web site. Few years ago, these logs were stored for a brief period of
time to calculate statics like popular pages ..etc. Now with Hadoop it is viable to store these click logs
for longer period of time.
3.4. Hadoop provides scalable analytics
There is no point in storing all the data, if we can't analyze them. Hadoop not only provides distributed
storage, but also distributed processing as well. Meaning we can crunch a large volume of data in parallel.
The compute framework of Hadoop is called Map Reduce. Map Reduce has been proven to the scale of
peta bytes.
Chapter 9, Introduction To MapReduce [31]
3.5. Hadoop provides rich analytics
Native Map Reduce supports Java as primary programming language. Other languages like Ruby, Python
and R can be used as well.
Of course writing custom Map Reduce code is not the only way to analyze data in Hadoop. Higher level
Map Reduce is available. For example a tool named Pig takes english like data flow language and translates
them into Map Reduce. Another tool Hive, takes SQL queries and runs them using Map Reduce.
Business Intelligence (BI) tools can provide even higher level of analysis. Quite a few BI tools can work
with Hadoop and analyze data stored in Hadoop. For a list of BI tools that support Hadoop please see this
chapter : Chapter 13, Business Intelligence Tools For Hadoop and Big Data [52]
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