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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
Languages
Concepts
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Authors (3):
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 R Patil R Patil
Author Profile Icon R Patil
R Patil
 Shindgikar Shindgikar
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Shindgikar
 Kumar Kumar
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Kumar
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Toc

Table of Contents (17) Chapters Close

Title Page
Copyright and Credits
Packt Upsell
Contributors
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 Index

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

The following is a sample command to define a Hive managed table:

Create Table < managed_table_name>  
   Column1 <data type>, 
   Column2 <data type>, 
   Column3 <data type> 
Row format delimited Fields Terminated by "t"; 

When the preceding query is executed, Hive creates the table and the metadata is updated in the metastore accordingly. But the table is empty. So...

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