What is HBase and How it is different than RDBMS?

HBase is an open-source, column-oriented distributed database system ina Hadoopenvironment. 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.

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.

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


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 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

Leave a Reply

Your email address will not be published. Required fields are marked *

Solve : *
20 × 17 =