Hive vs HBase

Definition of Hive vs HBase

Hive vs Hbase is Hadoop based solution used for big data. Both technologies we use to serve different purposes and real-time scenarios. We recommended using hive to analyze the data of time series, it evaluates a website and logs trends. Using a real-time query in a hive is not suggested because results take time to fetch the data. Hbase is used to perform real-time queries.

Difference Between Hive vs HBase

Basically, HDFS is the backbone of HBase. HBase is an open-source NoSQL database that contains columns and rows. Hive is a Hadoop-based data warehouse and is an open source, it searches and analyzes structured and semi-structured data which contains files of Hadoop.

Hbase is a key value storage that contains low latency also it will contain the ability to do the queries arbitrarily. In HBase, we can store data in a column-oriented format. Hive is nothing but a query engine, it’s developed to use with data repositories and it contains huge volumes. Hive is compatible with multiple file formats.

What is Hive?

Hive is nothing but a data warehouse package built on top of HDFS. We can use hive, mainly for data analysis. Hive targets users who are comfortable with SQL is very similar to SQL. In hive, we can manage and query the structured data. Hive abstracts the Hadoop complexity, basically, hive is developed by Facebook to handle a large amount of data. Hive contains the following features as follows.

• Hive is used to manage and search structured data from tables.
• Hive is quick and scalable and it uses new ideas.
• Hive schema is kept in the database and data is processed using HDFS.
• Hive contains three core parts as follows.
  1. Hive client
  2. Hive services
  3. Hive compute and storage

• Hive supports the RCFILE, text file, ORC, and sequence file formats.
• In hive first database and tables are created and then we can put data into tables from the right side.

What is HBase?

HDFS supports the HBase database, it is column-oriented. This project is scaled horizontally in multiple directions. HBase allows random access to structured data and also it uses a tolerance of Hadoop errors. It allows arbitrary read and write access on HDFS. A data consumer uses HBase to read data of HDFS. Below are the features of HBase as follows.

• Hbase is developed to do operations that contain low latency.
• In HBase, we can see a lot of action in read and write operations.
• Hbase stores a substantial quantity of information in table form.
• Hbase offers scalability into the modular and linear levels.
• We can share the data in an adjustable and automatic way.
• Hbase supports automatic failover.
• It will reduce the number of jobs for HBase tables.

Head to Head Comparison Between Hive vs HBase (Infographics)

Below are the top 13 differences between Hive vs HBase:

Key Differences between Hive vs HBase

Let us look at the key differences between Hive and HBase:

• Hive is based on a MapReduce technique and it is built on HDFS, we use HQL in the hive to query data. Hbase can be run on HDFS and Hadoop, it is an open-source NoSQL database and it is expandable also it holds unlimited data.
• The HBase process of transaction is known as OLTP, we can do this on the primary node, so it can process real-time data. The primary use of hive is to process batch, it falls under OLAP.
• Hbase supports data in an unstructured format, the data field map is defined by the user. Hive allows storage in structured as well as unstructured format. It offers built-in support for data types of SQL.
• Hbase creates adaptable, cheap, and easy-to-maintain HBGIS. In HBase, it is easy to extract random data from the dataset to use key values. Hive runs on SQL queries, it is provided language as HQL which is similar to SQL.
• HBase contains minimal latency, but it will contain the possibility of inconsistency and latency spikes occurring while using HBase. The latency of the hive is medium to high, it depends on the response of the computer. Basically, HBase and hive both are Hadoop and HDFS-based data warehouses and they are used to store lots of data.

Hive Requirement

To use or install the hive we need below minimum requirements. We need to install specified requirements at the time of installation of a hive in our system as follows.

• We require a minimum of 2 GB of memory, while it is recommended to use 4GB.
• We required graphics as AMD Radeon HD 7790, while it is recommended as AMD Radeon HD 2600 XT.
• We require a file size of 3 GB, also it is recommended the same as 3 GB.
• Hive is platform-independent, so we can install it on Linux as well as Windows.
• We require 2-core CPUs, while it is recommended to use 4 cores.
• The recommended disk speed IOPS is 50.

HBase Requirement

Basically, HBase uses caches to fill the memory, so we required high memory to use HBase. To install Hbase we require the following hardware as follows.

• We can install HBase on Linux as well as on Windows systems.
• To install HBase we require a min 1 GB of disk space.
• To store the log of HBase we require min 500 MB of disk space.
• To install HBase we need to create a separate Hadoop user.
• We require a java 1.7+ version to install HBase in our system.
• We need to install and configure HDFS to use HBase.

Comparison Table of Hive vs HBase

The table below summarizes the comparisons between Hive vs HBase:

Hive	HBase
Hive is nothing but a query engine, similar to queries of SQL.	HBase is a data storage used for unstructured data.
We are using hive mainly for batch processing.	HBase is mainly used for transaction processing.
We cannot use hive for real-time processing, so we cannot obtain immediate results.	We are using HBase for real-time processing, so we obtain immediate results.
Hive is used for analytical queries, used to analyze big data.	HBase is used for real-time queries.
We can run hive on top of Hadoop.	HBase runs on top of HDFS.
Hive is not a database.	HBase supports NoSQL database.
Hive contains the schema model.	HBase is not a schema model.
It contains high latency, as the processing of the batch takes time.	It contains low latency as compared to the hive.
Hive is expensive compared to HBase.	HBase is less expensive as compared to the hive.
Hive is used HQL.	To use CRUD operation HBase does not contain query language, HBase is a shell that we use to retrieve and edit data.
Hive contains eventual consistency of data.	HBase contains immediate consistency of data.
Hive does not support secondary indexes.	HBase, support secondary indexes.
An example of hive is Hubspot.	An example of HBase is Facebook.

Purpose of hive

The apache hive is a data warehouse system, used to handle batch processing. To handle the batch process we use hive. Hive provides a summarization of data, and query analysis onto large pools of unstructured data. Apache hive uses HQL it’s similar to SQL, to query data.

Hive supports transactions, and it also contains functionality to reduce constraints of table schema and required access on queries. In a nutshell, the hive provides SQL features onto spark or Hadoop data, and also handles multiple functions used in SQL.

Purpose of HBase

Apache HBase is NoSQL and its runs on HDFS. We can use HBase for real-time data processing. Hbase is the best suited for OLTP type of applications. Hbase contains multiple tables and those tables were split in multiple columns. In nutshell HBase processes and stores Hadoop data as per real-time needs of read and write operations in applications.

Hbase includes structured as well as unstructured data, as we know that HBase contains low latency also we can access the data by using command shells. Hbase contains storage layers in clusters of Hadoop which contains massive brands and it needs Hadoop storage.

Conclusion

Hbase is a key value storage it contains low latency also it can do the queries arbitrarily. In HBase, we can store the data in a column-oriented format. We recommended using hive to analyze the data of time series, it evaluates a website and logs trends.

Recommended Articles

This is a guide to Hive vs HBase. Here we discuss Hive vs HBase key differences with infographics and a comparison table in detail. You can also go through our other suggested articles to learn more –

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