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 architecture

The following is a representation of Hive architecture:

The preceding diagram shows that Hive architecture is divided into three parts—that is, clients, services, and metastore. The Hive SQL is executed as follows:

  • Hive SQL query: A Hive query can be submitted to the Hive server using one of these ways: WebUI, JDBC/ODBC application, and Hive CLI. For a thrift-based application, it will provide a thrift client for communication.
  • Query execution: Once the Hive server receives the query, it is compiled, converted into an optimized query plan for better performance, and converted into a MapReduce job. During this process, the Hive Server interacts with the metastore for query metadata.
  • Job execution: The MapReduce job is executed on the Hadoop cluster.