Difference between Hive and HBase

Hive and HBase are Hadoop-based Big Data solutions. These technologies serve different purposes in almost any real use scenario. When you log onto Facebook, you may see your friend's list, a news feed, ad suggestions, friend suggestions, etc. Twitter is similar.

Apache Hadoop, along with other technologies we'll explore today, such as Apache Hive vs. Apache HBase, is how Facebook loads all of its messy data in a presentable manner. Apache Hadoop enables Facebook's two billion-plus daily users.

Because Big Data systems are complicated, all technologies must be used together. Hive is recommended for analyzing time-series data. It can evaluate trends and website logs. Real-time querying with the Hive is not suggested because results may take some time.

HBase is an excellent tool for performing real-time queries on Big Data.

What is HBase?

The Hadoop File System supports the column-oriented HBase database. This open-source project can scale horizontally in both directions. HBase mimics Google's large table. It allows random access to enormous amounts of structured data. It uses Hadoop's error tolerance. It allows arbitrary read-and-write access to Hadoop File System data in real time. One can store data in HDFS or HBase. Data consumers use HBase to read HDFS data. HBase, which sits above Hadoop, provides read/write data access.

Some features of HBase are −

  • HBase was developed specifically with low-latency operations in mind.

  • HBase sees a lot of action in the random read and write operations departments.

  • HBase is capable of storing a substantial quantity of information in the form of tables.

  • HBase is capable of storing a substantial quantity of information in the form of tables.

  • Offers scalability on both a linear and modular level across cluster environments.

  • Read and write operations must strictly adhere to this principle.

  • Sharding of table data that is both automatic and adjustable

  • Automatic failover support for servers located in different regions

  • Base classes that make supporting Hadoop Map more convenient.

  • Reduce the number of jobs in the HBase tables.

Application of HBase

There are a variety of uses for HBase across a wide range of industries, including the healthcare industry, the e-commerce business, and the sports sector. Take, for example −

  • HBase is utilized in the field of healthcare for the purpose of preserving gene sequence as well as the disease history of individuals or an entire region.

  • HBase is utilized in the field of healthcare for the purpose of preserving gene sequence as well as the disease history of individuals or an entire region.

  • In the world of athletics, HBase is the database of choice for storing match details as well as the history of each contest. It makes use of these facts in order to improve its predictions.

What is Hive?

Apache Hive is an open-source data warehouse software for reading, writing, and managing huge data set files in HDFS or Apache HBase. Hive lets SQL developers query and analyze data using HQL commands, similar to SQL. It simplifies MapReduce programming by eliminating the requirement to write Java code. Hive will automatically build the map and reduce functions if you write queries in HQL.

Some features of Hive are

  • Hive was made to search and manage only structured data in tables.

  • Hive is scalable, quick, and uses well-known ideas.

  • Schema is kept in a database, and data that has been processed is put in a Hadoop Distributed File System (HDFS)

  • First, tables and databases are made, and then data is put into the right tables.

  • Hive supports ORC, SEQUENCEFILE, RCFILE, and TEXTFILE file formats.

Hive consists of three core parts

  • Hive Client − Hive provides drivers for various applications. Thriftbased apps can communicate using a Thrift client.

  • Hive Services − Hive Services enables client interaction. Hive Services handles queries for clients.

  • Hive Storage and Computing Hive's "Meta storage database" stores table metadata. Table data and query results are saved in Hadoop's HDFS cluster.−

Difference between HBase and Hive

The following table highlights the major differences between HBase and Hive −

Basis of comparison
HDFS is the backbone for HBase, a column-oriented distributed database. It's an open-source NoSQL database with rows and columns.
Apache Hive is a Hadoop-based open-source data warehouse. It searches and analyses structured and semi-structured data contained in Hadoop files.
Key-value storage with low latency and the ability to do arbitrary queries on the data. The data is stored in a format that is column oriented.
Just like SQL, this query engine was developed for use with huge volume data repositories. It is compatible with a variety of file formats.
HBase is an open-source NoSQL database that runs on Apache Hadoop and HDFS. This expandable storage can hold unlimited data.
Hive is a Map Reducebased SQL engine built on HDFS. HQL is used to query HDFS data (Hive Query Language).
Transaction processing, often known as OLTP, is the primary application for HBase's utilization. In the case of HBase, however, real-time processing is a possibility.
As batch processing is the primary use for Hive, it falls under the category of OLAP. In the case of Hive, it is also impossible to perform processing in real time.
Data types
Only supports data in an unstructured format. The mappings from data field names to Java-supported data types are defined by the user.
Allows for the storage of both organized and unstructured data. Offers built-in support for typical SQL data types, such as INT, FLOAT, and VARCHAR, among others.
HBase creates a cheap, adaptable, and easy-to maintain Hadoop-based GIS (HBGIS). On-disk column storage format for sparse big data sets. It's easy to extract random data from a large dataset using key values.
Hive runs SQL queries on petabytes of Hadoop data. It also provides a similar to SQL query language called HQL for querying Hadoop node data.
Minimal, although there is a possibility of it being inconsistent. Latency spikes can occur as a consequence of the structural limits of the HBase design when subjected to strong write demands.
Medium to high, depending on computer responsiveness. Distributed execution offers better data performance than monolithic query systems like RDBMS.


Even though both HBase and Hive are Hadoop-based data warehouses used to store and process a lot of data, they store and query data in very different ways.

HBase is a column-oriented database management system used to store a lot of data. It also lets you store sparse data sets, which are common in many big data use cases.

Hive, on the other hand, is more like a traditional data warehouse reporting system. It is built on top of Hadoop and is used to run processing jobs on a schedule and then load the results into a summary table that client applications can then query.

Updated on: 28-Jul-2022

1K+ Views

Kickstart Your Career

Get certified by completing the course

Get Started