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)

Summary

In this chapter, we saw how data is stored and accessed using a Hadoop SQL interface called Hive. We studied various partitioning and indexing strategies in Hive. The working examples helped us to understand JSON data access and schema evolution using Avro in Hive. In the second section of the chapter, we studied a NoSQL data store called HBase and its difference with respect to RDBMS. The row design of the HBase table is very crucial to balancing reads and writes to avoid region hotspots. One has to keep in mind the HBase table design best practices discussed in this chapter. The working example shows the easier paths of data ingestions into an HBase table and its integration with Hive.

In the next chapter, we will take a look at tools and techniques for designing real-time data analytics.