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Modern Big Data Processing with Hadoop

You're reading from  Modern Big Data Processing with Hadoop

Product type Book
Published in Mar 2018
Publisher Packt
ISBN-13 9781787122765
Pages 394 pages
Edition 1st Edition
Languages
Concepts
Authors (3):
V Naresh Kumar V Naresh Kumar
Profile icon V Naresh Kumar
Manoj R Patil Manoj R Patil
Profile icon Manoj R Patil
Prashant Shindgikar Prashant Shindgikar
Profile icon Prashant Shindgikar
View More author details

Table of Contents (12) Chapters

Preface 1. Enterprise Data Architecture Principles 2. Hadoop Life Cycle Management 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

Hive data model management

Hive handles data in the following four ways:

  • Hive tables
  • Hive table partition
  • Hive partition bucketing
  • Hive views

We will see each one of them in detail in the following sections.

Hive tables

A Hive table is very similar to any RDBMS table. The table is divided into rows and columns. Each column (field) is defined with a proper name and datatype. We have already seen all the available datatypes in Hive in the Supported datatypes section. A Hive table is divided into two types:

  • Managed tables
  • External tables

We will learn about both of these types in the following sections.

Managed tables

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