Difference between Hive and HBase

Last Updated : 06 Mar, 2023

Hive and HBase are both Apache Hadoop-based technologies, but they have different use cases and characteristics:

Data Model: Hive uses a SQL-like language called HiveQL to process structured data stored in Hadoop Distributed File System (HDFS). HBase, on the other hand, is a NoSQL database that stores unstructured or semi-structured data in a column-family data model.

Processing: Hive provides a batch processing framework that enables users to write queries using HiveQL, which are then translated into MapReduce jobs and executed on Hadoop. HBase, on the other hand, is designed for real-time processing of big data and supports random read and write operations.

Schema: Hive requires a predefined schema to be defined before data can be stored and processed. HBase, on the other hand, does not require a schema to be defined beforehand and allows for more flexible data modeling.

Querying: Hive is optimized for OLAP (Online Analytical Processing) and data warehousing, making it suitable for complex queries and ad hoc analysis. HBase, on the other hand, is optimized for OLTP (Online Transaction Processing) and is suitable for real-time queries on large datasets.

Data Size: Hive is designed to handle large volumes of data and can handle petabyte-scale data warehouses. HBase is also designed for large-scale data, but it is more suitable for storing and processing real-time, high-velocity data.

Hive and HBase differ in their data model, processing, schema, querying, and data size characteristics. Hive is more suitable for complex queries and ad hoc analysis, while HBase is more suitable for real-time queries on large datasets.

Hive: Hive is a data warehousing package built on the top of Hadoop. It is mainly used for data analysis. It generally targets users already comfortable with Structured Query Language (SQL). It is very similar to SQL and is called Hive Query Language (HQL). Hive manages and queries structured data. Moreover, hive abstracts the complexity of Hadoop. Hive was developed by Facebook in 2007 to handle the massive amount of data. It does not support:

Not a full database.
Not a real-time processing system.
Not SQL-92 compliant.
Does not provide row-level inserts, updates, or deletes.
Doesn’t support transactions and limited sub-query support.
Query optimization in an evolving stage.

Hbase: HBase is a column-oriented database management system that runs on top of the Hadoop Distributed File System (HDFS). It is well suited for sparse data sets, which are common in many big data use cases. It is an open-source, distributed database developed by Apache software foundations. Initially, it was named Google Big Table, afterwards, it was re-named HBase and is primarily written in Java. It can store a massive amount of data from terabytes to petabytes. It is built for low-latency operations and is used extensively for reading and writing operations. It stores a large amount of data in the form of tables.

Difference between Hive and HBase:

S. No.	Parameters	Hive	HBase
1.	Basics	Hive is a query engine that uses queries that are mostly similar to SQL queries.	It is Data storage, particularly for unstructured data.
2.	Used for	It is mainly used for batch processing (that means OLAP-based).	It is extensively used for transactional processing (that means OLTP).
3.	Processing	It cannot be used for real-time processing since immediate analysis results are unable to obtain. In other words, the operations in Hive require batch processing, they normally take a long time to complete.	It can be used to process data in real-time. Transactional operations are faster than non-transactional operations ( since HBase stores data in the form of key-value pairs).
4.	Queries	It is used only for analytical queries. It is mostly used to analyze Big Data.	It is used for real-time querying. It is mostly used to query Big Data.
5.	Runs on	Hive runs on the top of Hadoop.	HBase runs on the top of HDFS (Hadoop Distributed File System).
6.	Database	Apache Hive is not a database.	It supports the NoSQL database.
7.	Schema	It has a schema model.	It is free from the schema model.
8.	Latency	Made for high latency operations as batch processing takes time.	Made for low-level latency operations.
9.	Cost	It is expensive as compared to HBase.	It is cost-effective as compared to Hive.
10.	Query Language	Hive uses HQL (Hive Query Language).	To conduct CRUD (Create, Read, Update, and Delete) activities, HBase does not have a specialized query language. HBase includes a Ruby-based shell where you can use Get, Put, and Scan functions to edit your data.
11.	Level of Consistency	Eventual consistency	Immediate consistency
12.	Secondary Indexes	It does not support Secondary Indexes.	It supports Secondary Indexes.
13.	Example	Hubspot	Facebook