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Many corporations are finding that the size of their data sets are outgrowing the capability of their systems to store and process them. The data is becoming too big to manage and use with traditional tools. The solution: implementing a big data system.
As Big Data Made Easy: A Working Guide to the Complete Hadoop Toolset shows, Apache Hadoop offers a scalable, fault-tolerant system for storing and processing data in parallel. It has a very rich toolset that allows for storage (Hadoop), configuration (YARN and ZooKeeper), collection (Nutch and Solr), processing (Storm, Pig, and Map Reduce), scheduling (Oozie), moving (Sqoop and Avro), monitoring (Chukwa, Ambari, and Hue), testing (Big Top), and analysis (Hive).
The problem is that the Internet offers IT pros wading into big data many versions of the truth and some outright falsehoods born of ignorance. What is needed is a book just like this one: a wide-ranging but easily understood set of instructions to explain where to get Hadoop tools, what they can do, how to install them, how to configure them, how to integrate them, and how to use them successfully. And you need an expert who has worked in this area for a decade—someone just like author and big data expert Mike Frampton.
Big Data Made Easy approaches the problem of managing massive data sets from a systems perspective, and it explains the roles for each project (like architect and tester, for example) and shows how the Hadoop toolset can be used at each system stage. It explains, in an easily understood manner and through numerous examples, how to use each tool. The book also explains the sliding scale of tools available depending upon data size and when and how to use them. Big Data Made Easy shows developers and architects, as well as testers and project managers, how to:
- Store big data
- Configure big data
- Process big data
- Schedule processes
- Move data among SQL and NoSQL systems
- Monitor data
- Perform big data analytics
- Report on big data processes and projects
- Test big data systems
Big Data Made Easy also explains the best part, which is that this toolset is free. Anyone can download it and—with the help of this book—start to use it within a day. With the skills this book will teach you under your belt, you will add value to your company or client immediately, not to mention your career.
- Sales Rank: #1951764 in Books
- Published on: 2014-12-23
- Released on: 2014-12-24
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x .89" w x 7.50" l, 1.48 pounds
- Binding: Paperback
- 392 pages
About the Author
Mike Frampton has been in the IT industry since 1990, working in many roles (tester, developer, support, QA), and in many sectors ( telecoms, banking, energy, insurance). He has also worked for major corporations and banks, including IBM, HP, and JPMorgan Chase. The owner of Semtech Solutions, an IT/Big Data consultancy, Mike currently lives by the beach in Paraparaumu, New Zealand, with his wife and son.
Most helpful customer reviews
0 of 0 people found the following review helpful.
Recommended book!
By kivosias
Very detailed.
Explains you the basics of Big Data and all you need to know to get started.
Recommended book!
5 of 5 people found the following review helpful.
An easy way to learn big Data via Hadoop and it tools
By Ian Stirk
Hi,
I have written a detailed chapter-by-chapter review of this book on www DOT i-programmer DOT info, the first and last parts of this review are given here. For my review of all chapters, search i-programmer DOT info for STIRK together with the book's title.
This book sets out to be "A Working Guide to the Complete Hadoop Toolset" and is both wide ranging in content, and practical in its approach. Michael Frampton assumes some knowledge of Linux and SQL (but only a little), and no knowledge of Hadoop or its tools.
There is a step by step approach to tool download, installation, execution, and error checking. The following areas of functionality and associated tools are covered:
*Hadoop installation (version 1 and version 2)
*Web-based data collection (Nutch, Solr, Gora, HBase)
*Map Reduce programming (Java, Pig, Perl, Hive)
*Scheduling (Fair and Capacity schedulers, Oozie)
*Moving data (Hadoop commands, Sqoop, Flume, Storm)
*Monitoring (Hue, Nagios, Ganglia)
*Hadoop cluster management (Ambari, CDH)
*Analysis with SQL (Impala, Hive, Spark)
*ETL (Pentaho, Talend)
*Reporting (Splunk, Talend)
Below is a chapter-by-chapter exploration of the topics covered.
Chapter 1: The Problem with Data
This chapter opens with a review of what big data is – data that can’t be stored and processed in the required timeframe using traditional methods (e.g. relational databases). It is typically defined in terms of the 3Vs (i.e. volume, velocity and variety).
Various attributes of big data are discussed, including: massive scalability, commodity and cost-effective hardware, ability to recover from hardware failure, and parallel processing.
Increasing amounts of data are being created and stored. Big data processing typically starts with high terabyte amounts of data, however big data can also be applicable to smaller systems which grow. Examples of high volume data generators are given, including the Large Hadron Collider which produces 15PB of detector data per year. The chapter ends with an overview of the rest of the book.
This is a useful opening chapter, defining what big data is, its attributes, and the problems it solves. It helpfully explains the book’s practical approach i.e. introduce a tool, show how to obtain it, how to install it, and show example usage. There’s a very useful overview of each subsequent chapter.
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Conclusion
This book provides a broad and practical introduction to big data, using Hadoop and its many tools. It gives comprehensive step by step instructions on how to download, install, configure, and run the various tools - additionally, common errors are explained and solutions proposed.
The book is easy to read, with helpful explanations, screenshots, listings, outputs, and a logical flow between the sections and chapters. There are good links between the chapters, and to websites containing further information. It also steps back and puts what’s being discussed into the larger context of big data. The book will certainly give you more confidence in the topic.
It should be noted that there is much more information available on all the tools discussed, however this book is a great starting point, and it does an excellent job of introducing the many tools in an easily understandable manner. This is perhaps the book to read before the more specific or advanced books (e.g. “Hadoop: The Definitive Reference” - the new edition of which is out in April 2015, look out for its review soon.)
If I have one concern, it relates to who will use the whole book. This book contains both admin and development sections, however large companies typically separate out their admin and development teams.
If you want a useful working introduction and overview of the current state of big data, Hadoop and its associated tools, I can highly recommend this very instructive book.
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