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)

Hadoop file formats

In Hadoop, there are many file formats available. A user can select any format based on the use case. Each format has special features in terms of storage and performance. Let's discuss each file format in detail.

Text/CSV file

Text and CSV files are very common in Hadoop data processing algorithms. Each line in the file is treated as a new record. Typically, each line ends with the n character. These files do not support column headers. Hence, while processing, an extra line of the code is always required to remove column headings. CSV files are typically compressed using GZIP codec because they do not support block level compression; it adds to more processing costs. Needless to mention they do not...