Book Image

Modern Big Data Processing with Hadoop

By : V Naresh Kumar, Manoj R Patil, Prashant Shindgikar
Book Image

Modern Big Data Processing with Hadoop

By: V Naresh Kumar, Manoj R Patil, Prashant Shindgikar

Overview of this book

The complex structure of data these days requires sophisticated solutions for data transformation, to make the information more accessible to the users.This book empowers you to build such solutions with relative ease with the help of Apache Hadoop, along with a host of other Big Data tools. This book will give you a complete understanding of the data lifecycle management with Hadoop, followed by modeling of structured and unstructured data in Hadoop. It will also show you how to design real-time streaming pipelines by leveraging tools such as Apache Spark, and build efficient enterprise search solutions using Elasticsearch. You will learn to build enterprise-grade analytics solutions on Hadoop, and how to visualize your data using tools such as Apache Superset. This book also covers techniques for deploying your Big Data solutions on the cloud Apache Ambari, as well as expert techniques for managing and administering your Hadoop cluster. By the end of this book, you will have all the knowledge you need to build expert Big Data systems.
Table of Contents (12 chapters)

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