Hadoop Application Architectures : Designing Real-World Big Data Applications by Ted Malaska, Rajat (Mark) Grover, Jonathan Seidman and Gwen Shapira (2015, Trade Paperback)
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ISBN: 1491900083. Hadoop Application Architectures: Designing Real-World Big Data Applications. Author: Grover, Rajat (Mark); Malaska, Ted; Seidman, Jonathan; Shapira, Gwen. Condition: New. Qty Available: 1.
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About this product
Product Identifiers
PublisherO'reilly Media, Incorporated
ISBN-101491900083
ISBN-139781491900086
eBay Product ID (ePID)205609567
Product Key Features
Number of Pages397 Pages
Publication NameHadoop Application Architectures : Designing Real-World Big Data Applications
LanguageEnglish
Publication Year2015
SubjectSystems Architecture / General, Programming / Parallel, Databases / Data Mining, Databases / General
TypeTextbook
AuthorTed Malaska, Rajat (Mark) Grover, Jonathan Seidman, Gwen Shapira
Subject AreaComputers
FormatTrade Paperback
Dimensions
Item Height1 in
Item Weight24.6 Oz
Item Length9.2 in
Item Width7 in
Additional Product Features
Intended AudienceScholarly & Professional
LCCN2015-514247
Dewey Edition23
IllustratedYes
Dewey Decimal005.7
SynopsisGet 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