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Hadoop Application Architectures, by Mark Grover, Ted Malaska, Jonathan Seidman, Gwen Shapira
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Get expert guidance on architecting end-to-end data management solutions with Apache Hadoop. While many sources explain how to use various components in the Hadoop ecosystem, this practical book takes you through architectural considerations necessary to tie those components together into a complete tailored application, based on your particular use case.
To reinforce those lessons, the book’s second section provides detailed examples of architectures used in some of the most commonly found Hadoop applications. Whether you’re designing a new Hadoop application, or planning to integrate Hadoop into your existing data infrastructure, Hadoop Application Architectures will skillfully guide you through the process.
This book covers:
- Factors to consider when using Hadoop to store and model data
- Best practices for moving data in and out of the system
- Data processing frameworks, including MapReduce, Spark, and Hive
- Common Hadoop processing patterns, such as removing duplicate records and using windowing analytics
- Giraph, GraphX, and other tools for large graph processing on Hadoop
- Using workflow orchestration and scheduling tools such as Apache Oozie
- Near-real-time stream processing with Apache Storm, Apache Spark Streaming, and Apache Flume
- Architecture examples for clickstream analysis, fraud detection, and data warehousing
- Sales Rank: #166975 in eBooks
- Published on: 2015-06-30
- Released on: 2015-06-30
- Format: Kindle eBook
About the Author
Mark is a committer on Apache Bigtop and a committer and PMC member on Apache Sentry (incubating) and a contributor to Apache Hadoop, Apache Hive, Apache Sqoop and Apache Flume projects. He is also a section author of O’Reilly’s book on Apache Hive – ProgrammingHive.
Ted is a Senior Solutions Architect at Cloudera helping clients be successful with Hadoop and the Hadoop ecosystem. Previously, he was a Lead Architect at the Financial Industry Regulatory Authority (FINRA), helping build out a number of solutions from web applications and Service Oriented Architectures to big data applicatons. He has also contributed code to Apache Flume, Apache Avro, Yarn, and Apache Pig.
Jonathan is a Solutions Architect at Cloudera working with partners to integrate their solutions with Cloudera’s software stack. Previously, he was a technical lead on the big data team at Orbitz Worldwide, helping to manage the Hadoop clusters for one of the most heavily traffickedsites on the internet. He's also a co-�founder of the Chicago Hadoop User Group and Chicago Big Data, technical editor for Hadoop in Practice, and has spoken at a number of industry conferences on Hadoop and big data,
Gwen is a Solutions Architect at Cloudera. She has 15 years of experience working with customers to design scalable data architectures. Formerly a senior consultant at Pythian,Oracle ACE Director and board member at NoCOUG. Gwen is a frequent speaker at industry conferences and maintains a popular blog.
Most helpful customer reviews
11 of 11 people found the following review helpful.
Highly recommended book about Hadoop best practices and example architectures
By Ian Stirk
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 Applications
Chapter 1 Data Modeling in Hadoop
The 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.
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Conclusion
This 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.
2 of 2 people found the following review helpful.
By far, the best technical book I've read in 10 years!
By 88volt
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.
1 of 1 people found the following review helpful.
Well written guide of Hadoop designing
By Vitek Filip
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.
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