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Product details

File Size: 20118 KB

Print Length: 400 pages

Simultaneous Device Usage: Unlimited

Publisher: O'Reilly Media; 1 edition (June 30, 2015)

Publication Date: June 30, 2015

Sold by: Amazon Digital Services LLC

Language: English

ASIN: B010ORQ8BS

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Amazon Best Sellers Rank:

#483,557 Paid in Kindle Store (See Top 100 Paid in Kindle Store)

Wow, this is an impressive book on Hadoop. The content is rich and comprehensive. Normally, I'd expect to read 3-4 books to cover the same amount of material. It reads well and the chapters are methodically laid out. Kudos to the authors for crafting such a well written book.

I bought this book because it was the only one that I could find that touched on data modeling in hadoop. Most of the materials I've read on hadoop focus solely ok the tooling and give little if any advice on how to structure your data - when to denormalize, when not to and what to do about those expensive joins. This book touched on that although I would have preferred that not being in chapter 9. So far a good read, I've not finished the book but I love the fact that they give rationale for using specific tools for specific jobs.

This book was exactly what I needed: Well-organized, easy to find what I need when "emergency" questions are arising, but also thorough enough to give me a decent start on the path to actually understanding not just WHAT to do, but WHY to do it like "this" instead of "that."

Well written guide that distinguishes how deep you are involved, enhances your insight whatever the emtry level in Hadoop was. Some of the suggestions on tools are getting already outdated with time running since book release, but basics still hold true.

a really good book to advance career in Big Data systems.

This book is great for getting your bearings with respect to the major functions of Hadoop, and the components that serve those functions. The examples are well thought-out and subtleties are pointed out and discussed.

Very much satisfied with the quality of the product.

Hi,I have written a detailed chapter-by-chapter review of this book on[...], 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 aims to provide best practices and example architectures for Hadoop technologists, how does it fare?This book is written for developers and architects that are already familiar with Hadoop, who wish to learn some of the current best practices, example architectures and complete implementations. It assumes some existing knowledge of Hadoop and its components (e.g. Flume, HBase, Pig, and Hive). Book references are provided for those needing topic refreshers. Additionally, it’s assumed you are familiar with Java programming, SQL and relational databases. It consists of two sections, the first of which has seven chapters and looks at factors that influence application architectures. The second consists of three chapters, each providing a complete end-to-end case study.Below is a chapter-by-chapter exploration of the topics covered.Section I Architectural Considerations for Hadoop ApplicationsChapter 1 Data Modeling in HadoopThe chapter opens with a look at storage considerations. Various file types are discussed, and the importance of spilltable compressed data highlighted. Avro and Parquet are generally the preferred file formats for row and columnar based storage respectively.The chapter continues with at look at factors to consider when storing data in HDFS. Directory structures are recommended (e.g. /users/). If you know what tools you intend to use to process the data (e.g. Hive), you can take advantage of partitioning – reduces IO, bucketing – improves performance of joins, and denormailization – eliminates the need for joining data.Factors to consider when storing data in HBase are discussed next. HBase is a NoSQL database, often thought of as a huge distributed hash table. This key-value store is optimized for fast lookups, and is especially suitable for problems having relatively few get and put requests. HBase tables can have millions of columns and billions of rows. Important considerations for choosing the row key are discussed. Other aspects of HBase covered include: use of timestamps, hops, tables and regions, and the use of column families.The chapter ends with a look at metadata, describing what metadata is, and why it’s important. The importance of the Hive metastore and its reuse by other tools is discussed.This chapter provides a useful discussion of features to consider in data modeling. Some sections seem wordy, but probably need to be so. Some useful recommendations are given (e.g. use the Avro file format), together supporting reasons.From its start, it’s clear this is not a book for beginners. The chapter is well written, has useful explanations, discussions, diagrams, references, links to other chapters, and considered recommendations. A useful chapter conclusion is provided. These features apply to the whole book....ConclusionThis book aims to provide Hadoop current best practices, example architectures and complete implementations – and succeeds in each area.The book is well written, providing good explanations, examples, walkthroughs, and diagrams. Useful links are given between chapters, and there’s a valuable conclusion at the end of each chapter. The order of the chapters is helpful in understanding the flow of topics. This is not a book for beginners, but does contain useful references to books to get you up to speed.In many ways, this book follows on naturally from “Hadoop: The Definitive Guide”, which I recently reviewed. It provides practical discussions of the many factors to consider when presented with common Hadoop architectural concerns (e.g. whether to use HDFS or HBase?). The book offers recommendations, and provides supporting information that backs these up.The book doesn’t cover all Hadoop technologies (e.g. it omits Machine Learning), but it does cover many popular ones. Some of the books referenced are getting old and some chapters have footnotes at the end, which would be better placed on the pages where they are referenced.Hadoop is changing rapidly, this book suggests the near future will see a decline in MapReduce processing, and a rise in processing using Spark. Similarly, at the higher-level of abstraction, SQL in its various flavours also appears to be in ascendancy.If you want to know the current state of Hadoop and its components, want a practical discussion of the pros and cons for using various tools, and want solutions to common problems, I can highly recommend this book.

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