HBase vs Hive : Feature Wise Difference between Hive vs HBase

Both Apache Hive and HBase are Hadoop based Big Data technologies. Also, both serve the same purpose that is to query data. However, Apache Hive and HBase both run on top of Hadoop still they differ in their functionality. 

So, in this blog “HBase vs Hive”, we will understand the difference between Hive and HBase. Moreover, we will compare both technologies on the basis of several features. But before going directly into hive and HBase comparison, we will introduce both Hive and HBase individually.

So, let’s start HBase vs Hive.

Difference Between HBase vs Hive

i. What is Apache Hive?

Initially, Hive was developed by Facebook. Afterward, it is under the Apache software foundation. Moreover, it is an open source data warehouse.

Also, we use it for analysis and querying datasets. Moreover, it is developed on top of Hadoop as its data warehouse framework for querying and analysis of data is stored in HDFS.

In addition, it is useful for performing several operations. Such as data encapsulation, ad-hoc queries, & analysis of huge datasets. Moreover, for managing and querying structured data Hive’s design reflects its targeted use as a system.

ii. What is HBase?

HBase is a non-relational column-oriented distributed database. Basically, it runs on the top of HDFS. Moreover, it is a NoSQL open source database that stores data in rows and columns. However, Cell is the intersection of rows and columns.

HBase vs Hive

Following points are feature wise comparison of HBase vs Hive.

Feature Wise Comparison – HBase vs Hive

i. Database type

• Apache Hive

Basically, Apache Hive is not a database.

• HBase

HBase does support NoSQL database.

ii. Type of processing

• Apache Hive

Hive does support Batch processing. That is OLAP.

• HBase

HBase does support real-time data streaming. That is OLTP.

iii. Data Schema

• Apache Hive

Basically, it supports to have schema model.

• HBase

However, it is schema-free.

iv. Latency

• Apache Hive

Apache Hive has high latency as compared to HBase.

• HBase

As compared to Hive, Hbase have low latency.

v. Cost

• Apache Hive

When compared to HBase, it is more costly.

• HBase

It is cost-effective while compared to Apache Hive.

vi. Database model

• Apache Hive

Its DataBase model is a relational DBMS.

• HBase

Its DataBase model is wide column store

vii. SQL Support

• Apache Hive

Hive uses HQL(Hive query language)

• HBase

It does not use SQL

Viii. Partition methods

• Apache Hive

Hive uses sharding method for partition

• HBase

Similarly, HBase also uses sharding method for partition

ix. Consistency Level

• Apache Hive

Hive is eventual consistent in nature

• HBase

While HBase is immediate consistent in nature

x. When to use

• Apache Hive

While we do not want to write complex MapReduce code, we use Apache Hive.

• HBase

Similarly, while we want to have random access to read and write a large amount of data, we use HBase.

xi. Secondary indexes

• Apache Hive

No support for secondary indexes.

• HBase

It does support secondary indexes.

xii. Replication Methods

• Apache Hive

Hive have selectable replication factor

• HBase

As similar as Hive, it also has selectable replication factor

xiii. Examples

• Apache Hive

For Hive, Hubspot is an example.

• HBase

For HBase, Facebook is the best example.

Usage – HBase vs Hive

• Apache Hive

i. We can use Hive while we are familiar with SQL queries and concepts.
ii. While we perform analytical querying of historical data
iii. For Hive to fully unleash its processing and analytical prowess it is important to have structured data.
iv. However, Hive does not support Real-time analysis. So, HBase is the alternative for real-time analysis.
v. Especially, for data analysts

• HBase

i. While we have a large amount of data.
ii. It requires ACID properties, although they are not mandatory.
iii. While Data model schema is sparse.
iv. Also, while we need to scale applications gracefully.

Companies Using Hive and HBase

• Apache Hive

While it comes to market share, has approximately 0.3% of the market share. That means 1902 companies are already using Apache Hive in production. Like:

i. For ad-hoc querying, data mining and for user-facing analytics, “Scribd” uses Hive.
ii. For near real-time web analytics, Hive is an integral part of the Hadoop pipeline at “Hubspot”.
iii. For data mining and analysis of its 435 million global user base, “Chitika”, the popular online advertising network uses Hive.

• HBase

Here, also HBase has a huge market share. That is about 9/1%. Hence, it means approximately 6190 companies use HBase. Basically, for time series analysis or for clickstream data storage and analysis Companies uses HBase.

i. To store massive databases for the internet and its users, Originally HBase used at “Google”.
ii.  For real-time analytics, counting Facebook likes and for messaging, “Facebook” uses HBase.
iii. To store all the trading graphs, “FINRA” Financial Industry Regulatory Authority uses HBase.
iv. For storing the graph data, “Pinterest” uses HBase.
v. To personalize the content feed for its users, “Flipboard” uses HBase.

So, this was all in HBase vs Hive. Hope you like our explanation.

Conclusion

Hence, we have seen HBase vs Hive in detail, both are different technologies. Both offer different functionalities where Hive works by using SQL language and it can also be called as HQL and HBase use key-value pairs to analyze the data. Moreover, Hive and HBase work better together.

Since Hive has low latency and can process a huge amount of data, still it cannot maintain up-to-date data. Whereas HBase doesn’t support analysis of data but supports row-level updates on a large amount of data.

However, we have learned a complete comparison between HBase vs Hive. Still, if any query occurs feel free to ask in the comment section.