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Table of contents
Features of HBase
Importance of NoSQL Databases in Hadoop
Why HBase over other NoSQL models?
HBase Vs. Hive
Key Takeaways
Last Updated: Mar 27, 2024

HBase Features

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HBase is a column-oriented database that provides a dynamic database schema. Mainly it operates on top of the HDFS and also carries MapReduce jobs. Besides, for data processing, HBase also supports other high-level languages. Initially, it was Google Big Table; afterward, it was named HBase and is primarily written in Java.  

Also see, Multiple Granularity in DBMS

Features of HBase

  • Strictly consistent reads and writes.
    It can be used for high-speed requirements since it offers consistent reads and writes.
  • Automatic and configurable sharding of tables
    HBase offers automatic and manual splitting of regions into smaller subregions as soon as it reaches a threshold size to reduce I/O time and overhead.
  • Client API
    Easy to use Java API for client access.
  • Hadoop/HDFS integration
    Convenient base classes for backing Hadoop MapReduce jobs with Apache HBase tables.
  • Linear and modular scalability.
    In both linear and modular forms, HBase supports scalability. In addition, we can say it is linearly scalable.
  • Thrift gateway and REST-ful Web services
    Thrift gateway and a REST-ful Web service that supports XML, Protobuf, and binary data encoding options.
  • Real-time Processing
    Block cache and Bloom Filters for real-time queries.
  • High Throughput
    Due to the high security and easy management characteristics of HBase, it offers unprecedented high write throughput.
  • Schema-less
    Due to the high security and easy management characteristics of HBase, it offers unprecedented high write throughput.
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Importance of NoSQL Databases in Hadoop

Hadoop plays an important role in solving typical business problems in big data analytics by managing large data sets and giving the best solutions in this domain.

  • Data processing
  • Data validation
  • Data storing

It enables certain types of NoSQL distributed databases (such as HBase), allowing for data to be spread across thousands of servers, leading to a slight reduction in performance. Also, fetching results by applying queries on substantial data sets stored in Hadoop storage is challenging. NoSQL storage technologies give the best solution for faster querying on massive datasets.

Why HBase over other NoSQL models?

  • A table for a famous web application may have billions of rows if we want to locate a particular row from such a vast amount of data. 
  • HBase is the perfect choice because its query fetch time is less, and therefore it's used by most online analytics applications.
  • Traditional relational data models don't meet the performance requirements of massive databases. Apache HBase can handle these performance and processing limitations.
  • HBase stores data in the map form as key/value pairs in the column method. Where all the columns are grouped as Column families.
  • HBase gives a flexible data model and low latency access to small amounts of data stored in large data sets.
  • HBase on top of Hadoop will enhance distributed cluster set up throughput and performance. It leads to provides faster random reads and writes operations.


It is a declarative SQL-based tool used to analyze structured data. It is built on top of Hadoop. It provides the functionality of reading, writing, and managing large datasets residing in distributed storage. It runs SQL-like queries called HQL (Hive query language), which gets internally converted to MapReduce jobs.

Hive is mainly used in performing operations like

  • Data encapsulation
  • Ad-hoc queries
  • Analysis of huge datasets

HBase Vs. Hive

Hive HBase
Hive is a query engine. HBase is a data storage mainly for storing unstructured data.
Hive(Apache) is mainly used for batch processing, i.e., OLAP(online analytical processing). HBase is extensively used for translational processing.
Operations in Hive are transformed into MapReduce jobs. Operations in HBase are run in real-time on the database.
Hive is for analytic queries. HBase is for real-time querying.


RDBMS stands for Relational Database Management System. It moves data into a database, stores the data, retrieves it to be manipulated by applications, and keeps the data into a collection of tables related to standard fields, i.e., database table columns.


Column-oriented Row-oriented
Flexible schema, add columns on the fly. Fixed schema
Joins using MR- not optimized Optimized for joins.
Tight integration with MR Loosely integration process.
Horizontal scalability, i.e., just add hardware Hard to shard and scale
Good for semi-structured data as well as unstructured data. Good for structured data.
Good with sparse tables. It is not optimized for sparse tables.


Must Read Apache Server


  1. Is HBase open source?
    Yes, HBase(Apache) Apache is an open-source, NoSQL, distributed big data store. It allows strictly, random consistent, real-time access to petabytes of data. It is very effective for handling large, sparse datasets.
  2. Why is HBase NoSQL?
    Hbase is a NoSQL database because it runs on top of Hadoop and provides various benefits, including flexible data models, horizontal scaling, speedy queries, and ease of use for developers like any NoSQL database. It is suited for data represented in billions of rows that cannot be accommodated on the traditional RDBMS database.
  3. Which of the features does HBase not support?
    HBase does not support structured query language like SQL because it is not a relational data store. Here a master node operates the cluster and region servers to store portions of the tables and performs the work on the data.

Key Takeaways

In this article, we learned about the critical features of HBase and how it provides unique features and will solve typical industrial use cases. We compared HBase with different NoSQL databases, i.e., Hive and RDBMS. We also the advantages of HBase over all other models.

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