HBase Tutorial for Beginners: Learn in 3 Days!

Details: Last Updated: 10 June 2021

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 re-named 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 consists 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 Unique 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.

HBase Syllabus

Here is what we cover in this 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

Why Choose HBase?

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

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

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 HBase different from other NoSQL model

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 of 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.

Data Base 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
Data base 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

HBase VS RDBMS

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.

HBASE	RDBMS
Schema-less in database	Having fixed schema in database
Column-oriented databases	Row oriented data store
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

Summary:-

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