Big data is a whole new world in the technology universe and its popularity has expanded from being a relatively unknown term to a widely-adopted method for improving business activities. A lot of organisations have implemented results from big data analysis to their business strategies. In the recent years, the number of opportunities in big data has increased and if you want to be a part of this lucrative industry, you can start with a big data certification course.
A number of tools have been developed over the years to help big data professionals analyse data more effectively. HBase and Cassandra are two prominent database management systems that are used in big data. If you are confused about which one to learn first, this article will help in guiding you. Let’s discuss the difference between HBase and Cassandra
Let’s jump in:
It is a free and open-source distributed NoSQL database management system designed to handle large amounts of data across many commodity servers, providing high availability with no single point of failure.
Cassandra was originally developed by Avinash Lakshman and Prashant Malik at Facebook to power the Facebook inbox search feature. Facebook released Cassandra as an open-source project on Google code in July 2008. In 2009, it became an Apache Incubator project.
HBase is an open source, non-relational, distributed database modelled after Google’s BigTable and is written in Java. It is developed as part of Apache Software Foundation’s Apache Hadoop project and runs on top of HDFS (Hadoop Distributed File System), providing Bigtable-like capabilities for Hadoop.
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Popularity – The popularity of a tool is one of the important factors for its usage in the industry. The higher the popularity of a tool, the higher is its usefulness. According to DB-Engines, the popularity rank of Apache Cassandra is 8 with a total score of 124.12, while that of HBase is 15 with a total score of 63.62. This shows that Cassandra is more popular among big data professionals than HBase.
The Score: HBase 0: Cassandra 1
High-availability – Though nodes in both Cassandra and HBase are asymmetrical, Cassandra requires one to identify some nodes as seed nodes, which serve as concentration points for inter-cluster communication while on HBase, some nodes must be pressed into serving as master nodes. Thus, Cassandra guarantees high availability by allowing multiple seed nodes in a cluster, while HBase guarantees the same via standby master nodes
The Score: HBase 1: Cassandra 2
Triggers – In HBase, triggers are supported by the CoProcessor capability. They allow HBase to observe the get/put/delete events on a table and then execute the trigger logic. On the other hand, Cassandra does not support it.
The Score: HBase 2: Cassandra 2
Secondary Indexes – Hbase does not natively support secondary indexes while Cassandra supports secondary indexes on column families where the column name is known.
The Score: HBase 2: Cassandra 3
Read Load Balancing in a single Row – Hbase does not support Read Load Balancing in a single row while Cassandra supports it.
The Score: HBase 2: Cassandra 4
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Though there are some areas where Cassandra performs better, there is no clear winner. Each of the two has their own areas of advantage and it depends upon the task you are performing. However, the popularity of Cassandra in the big data universe is a bit more than HBase and it is advisable to learn it first. When you are comfortable with it, you can always learn HBase easily.