Chapter 1, Configuring HBase, provides in-depth knowledge of how to set up, configure, administer, and manage a large and scalable cluster.

Chapter 2, Loading Data, will deep dive into how we can extract data from various input sources using different process such as bulk load, put, and using MapReduce.

Chapter 3, Working with Large Distributed Systems I, talks about the internal architecture of HBase, how it connects, and provides a very scalable model.

Chapter 4, Working with Large Distributed Systems II, gives more details and is an extension of Chapter 3.

Chapter 5, Working with the Scalable Structure of Tables, allows us to understand how data can be modeled and how to design a scalable data model using HBase.

Chapter 6, HBase Client, allows the users to understand how we can communicate with core HBase using various type of clients.

Chapter 7, Large-Scale MapReduce, shows how to design a large scale MapReduce job using HBase, how the internals of it work, and how to optimize the HBase framework to do it.

Chapter 8, HBase Performance Tuning, will walk you through the process of fine-tuning read and write at consistent speeds, agnostic to the scale at which it's running.

Chapter 9, Performing Advance Task, will discuss some advance topics about machine learning using Mahout libraries and real-time text data analysis.

Chapter 10, Optimizing HBase for the Cloud, discusses how to utilize the Amazon cloud HBase framework.

Chapter 11, Case Study, integrates HBase with different search engines such as Solr, Elasticsearch, and Lily.