HBase Tutorial for Beginners: What is HBase? Learn in 3 Days!

HBase Tutorial Summary

Hbase is a column-oriented database management system that runs on top of HDFS (Hadoop Distributed File System). In this HBase tutorial for beginners, you will learn Apache HBase basics and advanced concepts. This HBase course contains all the HBase basics from introduction, installation, architecture to advanced stuff.

What is HBase?

HBase is an open-source, column-oriented distributed database system in a Hadoop environment. Initially, it was Google Big Table, afterward; it was renamed as HBase and is primarily written in Java.  Apache HBase is needed for real-time Big Data applications.

HBase can store massive amounts of data from terabytes to petabytes. The tables present in HBase consist of billions of rows having millions of columns. HBase is built for low latency operations, which is having some specific features compared to traditional relational models.

HBase Training Syllabus

Here is what we cover in this Apache HBase Training Guide

👉 Lesson 1 Architecture of HBase — HBase Architecture, Components, and Data Model
👉 Lesson 2 HBase Installation — HBase Installation on Ubuntu
👉 Lesson 3 HBase Shell Commands — Learn with Example
👉 Lesson 4 HBase Create Table — Steps to create a table in HBase using Java API
👉 Lesson 5 Insert & Retrieve Data in HBase — get(), put(), scan() Examples
👉 Lesson 6 Performance Bottlenecks in HBase — HBase Advantage and Limitations
👉 Lesson 7 Hbase Interview Questions — Top 30 Hbase Interview Questions & Answers

What will you learn in this HBase Tutorial for Beginners?

In this HBase tutorial for beginners, you will learn What Apache HBase is, the Architecture of HBase, How to install HBase, Steps to create a table in HBase, HBase Advantage and Limitations, etc.

Why Choose HBase?

A table for a popular web application may consist of billions of rows. If we want to search a particular row from such a huge amount of data, HBase is the ideal choice as query fetch time is less. Most of the online analytics applications use HBase.

Traditional relational data models fail to meet the performance requirements of very big databases. These performance and processing limitations can be overcome by Apache HBase.

Apache HBase Features

  • HBase is built for low latency operations
  • HBase is used extensively for random read and write operations
  • HBase stores a large amount of data in terms of tables
  • Provides linear and modular scalability over cluster environment
  • Strictly consistent to read and write operations
  • Automatic and configurable sharding of tables
  • Automatic failover supports between Region Servers
  • Convenient base classes for backing Hadoop MapReduce jobs in HBase tables
  • Easy to use Java API for client access
  • Block cache and Bloom Filters for real-time queries
  • Query predicate pushes down via server-side filters.

Importance of NoSQL Databases in Hadoop

In big data analytics, Hadoop plays a vital role in solving typical business problems by managing large data sets and gives the best solutions in analytics domain.

In the Hadoop ecosystem, each component plays its unique role for the

  • Data processing
  • Data validation
  • Data storing

In terms of storing unstructured, semi-structured data storage as well as retrieval of such data’s, relational databases are less useful. Also, fetching results by applying query on huge data sets that are stored in Hadoop storage is a challenging task. NoSQL storage technologies provide the best solution for faster querying on huge datasets.

Other NoSQL storage type Databases

Some of the NoSQL models present in the market are Cassandra, MongoDB, and CouchDB. Each of these models has different ways of storage mechanism.

For example, MongoDB is a document-oriented database from the NoSQL family tree. Compared to traditional databases, it provides the best features in terms of performance, availability, and scalability. It is an open-source document-oriented database, and it’s written in C++.

Cassandra is also a distributed database from open-source Apache software which is designed to handle a huge amount of data stored across commodity servers. Cassandra provides high availability with no single point of failure.

While CouchDB is a document-oriented database in which each document fields are stored in key-value maps.

How is HBase different from other NoSQL models

HBase storage model is different from other NoSQL models discussed above. This can be stated as follow.

  • HBase stores data in the form of key/value pairs in a columnar model. In this model, all the columns are grouped together as Column families.
  • HBase provides a flexible data model and low latency access to small amounts of data stored in large data sets.
  • HBase on top of Hadoop will increase the throughput and performance of distributed cluster set up. In turn, it provides faster random reads and writes operations.

Which NoSQL Database to choose?

MongoDB, CouchDB, and Cassandra are NoSQL type databases that are feature specific and used as per their business needs. Here, we have listed out different NoSQL database as per their use case.

DataBase Type Based on Feature Example of Database Use case (When to Use)
Key/ Value Redis, MemcacheDB Caching, Queue-ing, Distributing information
Column-Oriented Cassandra, HBase Scaling, Keeping Unstructured, non-volatile
Document-Oriented MongoDB, Couchbase Nested Information, JavaScript friendly
Graph-Based OrientDB, Neo4J Handling Complex relational information. Modeling and Handling classification.

HBase Vs. Hive

Features HBase Hive
DataBase model Wide Column store Relational DBMS
Data Schema Schema- free With Schema
SQL Support No Yes, it uses HQL(Hive query language)
Partition methods Sharding Sharding
Consistency Level Immediate Consistency Eventual Consistency
Secondary indexes No Yes
Replication Methods Selectable replication factor Selectable replication factor


While comparing HBase with Traditional Relational databases, we have to take three key areas into consideration. Those are data model, data storage, and data diversity.

  • Schema-less in database
  • Having fixed schema in database
  • Column-oriented databases
  • Row oriented datastore
  • Designed to store De-normalized data
  • Designed to store Normalized data
  • Wide and sparsely populated tables present in HBase
  • Contains thin tables in database
  • Supports automatic partitioning
  • Has no built in support for partitioning
  • Well suited for OLAP systems
  • Well suited for OLTP systems
  • Read only relevant data from database
  • Retrieve one row at a time and hence could read unnecessary data if only some of the data in a row is required
  • Structured and semi-structure data can be stored and processed using HBase
  • Structured data can be stored and processed using RDBMS
  • Enables aggregation over many rows and columns
  • Aggregation is an expensive operation


HBase provides unique features and will solve typical industrial use cases. As column-oriented storage, it provides fast querying, fetching of results, and a high amount of data storage. This course is a complete step-by-step introduction to HBase.