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Modern Big Data Processing with Hadoop

You're reading from   Modern Big Data Processing with Hadoop Expert techniques for architecting end-to-end big data solutions to get valuable insights

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Product type Paperback
Published in Mar 2018
Publisher Packt
ISBN-13 9781787122765
Length 394 pages
Edition 1st Edition
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Authors (3):
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 R Patil R Patil
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R Patil
 Shindgikar Shindgikar
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Shindgikar
 Kumar Kumar
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Kumar
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Toc

Table of Contents (12) Chapters Close

Preface 1. Enterprise Data Architecture Principles 2. Hadoop Life Cycle Management FREE CHAPTER 3. Hadoop Design Consideration 4. Data Movement Techniques 5. Data Modeling in Hadoop 6. Designing Real-Time Streaming Data Pipelines 7. Large-Scale Data Processing Frameworks 8. Building Enterprise Search Platform 9. Designing Data Visualization Solutions 10. Developing Applications Using the Cloud 11. Production Hadoop Cluster Deployment

How Hive works

Hive databases are comprised of tables which are made up of partitions. Data can be accessed via a simple query language and Hive supports overwriting or appending of data. Within a particular database, data in tables is serialized and each table has a corresponding HDFS directory. Each table can be sub-divided into partitions that determine how data is distributed within subdirectories of the table directory. Data within partitions can be further broken down into buckets.

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