- Tapa blanda: 512 páginas
- Editor: Manning Publications; Edición: 2 (12 de octubre de 2014)
- Colección: In Practice
- Idioma: Inglés
- ISBN-10: 1617292222
- ISBN-13: 978-1617292224
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nº270.412 en Libros en idiomas extranjeros (Ver el Top 100 en Libros en idiomas extranjeros)
- n.° 760 en Libros en idiomas extranjeros > Informática, internet y medios digitales > Sistemas operativos
- n.° 920 en Libros en idiomas extranjeros > Informática, internet y medios digitales > Bases de datos
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Hadoop in Practice (Inglés) Tapa blanda – 12 oct 2014
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Descripción del producto
Reseña del editor
Hadoop in Practice, Second Edition provides over 100 tested, instantly useful techniques that will help you conquer big data, using Hadoop. This revised new edition covers changes and new features in the Hadoop core architecture, including MapReduce 2. Brand new chapters cover YARN and integrating Kafka, Impala, and Spark SQL with Hadoop. You'll also get new and updated techniques for Flume, Sqoop, and Mahout, all of which have seen major new versions recently. In short, this is the most practical, up-to-date coverage of Hadoop available anywhere.
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
About the Book
It's always a good time to upgrade your Hadoop skills! Hadoop in Practice, Second Edition provides a collection of 104 tested, instantly useful techniques for analyzing real-time streams, moving data securely, machine learning, managing large-scale clusters, and taming big data using Hadoop. This completely revised edition covers changes and new features in Hadoop core, including MapReduce 2 and YARN. You'll pick up hands-on best practices for integrating Spark, Kafka, and Impala with Hadoop, and get new and updated techniques for the latest versions of Flume, Sqoop, and Mahout. In short, this is the most practical, up-to-date coverage of Hadoop available.
Readers need to know a programming language like Java and have basic familiarity with Hadoop.
- Thoroughly updated for Hadoop 2
- How to write YARN applications
- Integrate real-time technologies like Storm, Impala, and Spark
- Predictive analytics using Mahout and RR
- Readers need to know a programming language like Java and have basic familiarity with Hadoop.
About the Author
Alex Holmes works on tough big-data problems. He is a software engineer, author, speaker, and blogger specializing in large-scale Hadoop projects.
Table of Contents
- Hadoop in a heartbeat
- Introduction to YARN
- Data serialization—working with text and beyond
- Organizing and optimizing data in HDFS
- Moving data into and out of Hadoop
- Applying MapReduce patterns to big data
- Utilizing data structures and algorithms at scale
- Tuning, debugging, and testing
- SQL on Hadoop
- Writing a YARN application
PART 1 BACKGROUND AND FUNDAMENTALS
PART 2 DATA LOGISTICS
PART 3 BIG DATA PATTERNS
PART 4 BEYOND MAPREDUCE
Biografía del autor
Alex Holmes is a software engineer, author, speaker and blogger specializing in large-scale Hadoop projects and solving tough Big Data problems. Alex blogs at grepalex.com.
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Opiniones de clientes más útiles en Amazon.com
Good examples and a nice index. I've not read it cover to cover (like I did with The Definitive Guide), but this is an excellent reference to find specific solutions.
Of course, one book cannot possibly cover everything you need to know about Hadoop, MapReduce, Parquet, Kafka, Camus, YARN and other technologies. And the book's software examples assume that you have had some experience with Java, XML and JSON. However, Hadoop in Practice, Second Edition gives a very good and reasonably deep overview of the Hadoop world, spanning such major topic categories as background and fundamentals, data logistics, Big Data patterns, and moving beyond MapReduce.
I don't "love" Hadoop and MapReduce; my experiences with them have been a struggle. So I can't give this book five stars on the review scale. But I am happy to give it four solid stars, and I definitely recommend it to others.
"Hadoop in Practice" is filled with excellent real-world examples that show Hadoop in motion. Those real-world examples are broken down in a consumable format that includes 85 different use cases with accompanying solutions. Having an intermediate-level knowledge of Java will go a long way as you traverse the examples and source code included with the book.
My personal favorite chapter, Chapter 7, shows users how to leverage data structures and algorithms at scale. Whether it is describing and detailing the shortest-path algorithm or friends-of-friends algorithm, "Hadoop in Practice" simply does not skimp on providing legitimate use cases along with pseudo code before breaking down each implementation in MapReduce complete with Java code.
Overall, "Hadoop in Practice" is structured well and every chapter begins with a high-level overview and ends with a thorough summery. Most chapters include detailed diagrams that I found useful when trying to understand some of the more complex issues in the book - and trust me, there are many.