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Table of contents
The Hadoop Foundation and Ecosystem
Storing Big Data with HBase
Frequently asked questions
In big data processing, What is the role of HBase?
What type of database is HBase?
Does HBase support partitioning?
Explain the storage format of HBase data?
How does HBase store data?
Last Updated: Mar 27, 2024

Storing Big Data with Hbase

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Big data is a set of massive and complex data sets and volumes, including vast data, management capabilities, social media analytics, and real-time data. In contrast, Hadoop is an open-source framework that gives access to the storage and processing of big data in a distributed environment covering a collection of computers using simple programming models. It is designed to scale from single servers to thousands of machines, offering local computation and storage.

Now let’s understand the Hadoop Foundation and Ecosystem in detail.

The Hadoop Foundation and Ecosystem

Using specialty query languages or writing programs are not the only ways you connect with the Hadoop ecosystem. IT teams that operate infrastructures need to control Hadoop and the big data applications. Let's Look at some examples from the Hadoop ecosystem that help these constituencies.


  • Zookeeper
    It is Hadoop's way of coordinating all the elements of these distributed applications. Zookeeper as technology is simple, but its features are powerful. It would not be easy to create resilient, fault-tolerant distributed Hadoop applications without it.
  • Sqoop
    Sqoop (SQL-to-Hadoop) is a device that gives the capacity to transfer data from non-Hadoop data stores, convert the data into a form that will be usable by Hadoop, and then load the data into HDFS(Hadoop Distributed File System). Like Pig, Sqoop is a command-line tool. We can type Sqoop commands into the terminal and execute them simultaneously.
  • Pig and Pig Latin
    The pig was designed to make Hadoop more approachable and usable by non-developers. Pig is a script-based, interactive, or execution environment supporting Pig Latin, a language utilized to express data flows. It supports the processing and loading of input data with a series of operators that will produce the desired output by transforming the input data.

Now, let's deep dive into the storing process of Big Data with HBase.

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Storing Big Data with HBase

HBase is highly configurable and gives great flexibility to address massive amounts of data efficiently. Now let's understand how HBase can help address your significant data challenges.

  • HBase is a columnar database. Like relational database management systems (RDBMSs), it stores all data in tables with columns and rows.
  • The intersection of a column and row is called a cell. Each cell value contains a "version" attribute that is no more than a timestamp, distinctively selecting the cell.
  • Versioning tracks swap in the cell and makes it possible to redeem any version of the contents.
  • HBase stores the data in cells in decreasing order (using the timestamp), so a reader will always first choose the most current values.
  • Columns in HBase belong to a column family. The column family name is used to identify its family members.
  • The rows in HBase tables also have a key associated with them. The structure of the key is very flexible. It can be a computed value, a string, or another data structure.
  • The key is used to control access to the cells in the row, and they are stored in order from low to high value.
  • These features together make up the schema. It can alter new tables and column families after the database is up and running.


We will close the article now with frequently asked questions since you get some idea of Storing Big Data with HBase.

Frequently asked questions

In big data processing, What is the role of HBase?

HBase is a  non-relational column-oriented database management system that runs on top of the Hadoop Distributed File System (HDFS). It gives a fault-tolerant way of storing sparse data sets, standard in many big data use cases.

What type of database is HBase?

HBase is a column-oriented, non-relational database that suggests that data is stored in separate indexes and columns by a solitary row key. This architecture permits rapid retrieval of individual rows and columns and efficient scans over different columns within a table.

Does HBase support partitioning?

Yes, Hbase supports partitioning. It does data partition based on non-overlapping, sorted key ranges around regional servers in the file format of HFile. Within each HFile, data will be available based on the critical value and the column name.

Explain the storage format of HBase data?

HFiles are storage files created to store HBase's data efficiently and fast. The HMaster is responsible for assigning the regions to each HRegionServer when HBase is started. It is responsible for managing everything related to rows, tables, and coordination activities.

How does HBase store data?

Data is kept as byte arrays in the cells of the HBase table because there are no data types in HBase. When the value is kept in the cell, the cell's content or value is categorized by the timestamp. So each cell of an HBase table may contain multiple versions of data.


In this article, we have extensively discussed Storing Big Data with Hbase. We discussed the Hadoop Foundation and Ecosystem and how to store Big Data with HBase.

After reading about Storing Big Data with Hbase, are you not feeling excited to read/explore more articles on the topic of HBase? Don't worry; Coding Ninjas has you covered. To learn, see  Architecture of HbaseHbase FeaturesHbase Troubleshooting, and Hbase Shell.

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