The success of your app development process depends on the database management system at a greater level. More or less we all know that. But most of us get confused when it comes to choosing the best NoSQL database management system. And the confusion increases when the choices are HBase and Cassandra. You may say that it’s just like choosing the best one from a twin. Yes up to an extent both of these databases are identical. But they have some differences too.

Day by day the demand for proper data management is increasing in all the business areas. And that’s why now NoSQL databases seem the true saviors. NoSQL databases come with the right set of technologies that simply help us to meet the increasing volume, velocity, and variety of data management requirements. 

Today we at Vyrazu Labs have picked two Not only SQL gems to recognize the best one, no matter how much identical twins they seem! In the below sections, we are going to create a solid HBase vs Cassandra battlefield so that we all can recognize the best one easily. But before that, we will give you brief introductions of both of these gems. If you are already aware of them, you can directly jump into the battlefield. But if you are a beginner or want to read a complete guide, read all the sections of this much-awaited blog. 

Reasons to pick a NoSQL database

• Able to manage a large volume of data irrespective of the data type
• It easily scales up for large data volume
• Comes with great memory and CPU capabilities
• No strict limitation for cache dependent read and write operations
• No database breakage
• No RDBMS model in NoSQL options 

HBase: HBase vs Cassandra

Apache HBase is a great name when it comes to choosing a reliable NoSQL database in 2020. It runs on the Hadoop Distributed File System (HDBS). Its operations run in real-time on its database instead of MapReduce jobs. This NoSQL database is divided into various tables and each table is split into many column families. Here in HBase, each key pair is known as a cell. And each cell consists of a column family, row-key, time-stamp, and column. Each row in the HBase is also a group of key mappings identified by the row-key. 

It works by storing data as the key/value. And it mainly supports 4 primary operations-

• Adding or updating rows
• Scans to retrieve a range of cells
• Returns cells to a specific row
• Deletes rows, columns, or column versions

Here versioning is also available so that fetching previous values of data can be easy and fast. Apart from this, it also includes tables. For tables and column families, the schema will be needed. But in the case of the column, there is no need for schema as the column includes counter functionality.

It works on multiple physical servers simultaneously. The servers may not operate together yet the operation will be performed smoothly. Generally, it uses two processes to ensure smooth operations such as-

Region Server- the term itself is making you understand that it is able to support various regions or record arrays. Regions or record arrays contain mainly 4 elements such as persistent storage, block cache, Memstore, and WAL. 

Master Server- we can call it the primary server of HBase. It is here to successfully maintain the region distribution across the region server. Apart from this, it also monitors the regions and manages ongoing tasks. 

In order to coordinate action between servers, HBase uses Apache ZooKeeper. Apache ZooKeeper is mainly a service for configuration and service sync management. 

Now it comes to focus on the advantages as well as disadvantages of HBase. Only from the structure definition or work process, recognizing a NoSQL database will be tough rather it will give us a vague idea. So, let’s take a glance at both of its advantages and drawbacks-

Advantages

• It is an amazing option for analytics especially in combination with Hadoop MapReduce
• It can easily handle a very large volume of data
• It supports scaling out in association with the Hadoop file system
• It offers a free license 
• There is no fixed schema so that you can design schema flexibly 
• Auto sharding and auto-failover
• Easy to understand rather a simple client interface 
• Row-level atomicity 

Drawbacks

• Here you will get no transaction support
• Single point of failure
• And here JOINS get handled in the MapReduce layer

Cassandra: HBase vs Cassandra

When your concerns are constant availability, high scalability, smooth performance, operational simplicity, and standard security, you can choose Apache Cassandra without any doubt. It is a leading NoSQL option in 2020. It does not only provide the mentioned benefits but also it is a cost-effective solution. 

Cassandra comes with decentralized architecture and provides availability, partition, and tolerance from the CAP theorem. Here any node can perform any operation. According to experts, Cassandra is rich with single-row performance ability. And for the use case, there are required consistency semantics. Here we will get Cassandra quorum reads. These are needed for strong consistency and these are slower than HBase reads. It can be limited in certain use cases as it does not support range based row scans. It will be a good option for the single row queries or based on a column value index, selecting multiple rows. 

Many people choose Cassandra when it comes to creating reliable and scalable repositories of data arrays. These data arrays are known as a hash. Cassandra works with keyspace. It mainly aligns the concept of the database schema in the relational model. The main intention of the Cassandra architecture is to get a P2P distributed system. This system is mainly made of node clusters. In this way, a node will be able to accept the read or write requests. In the cluster, every node shares the state information about itself and other nodes pick the P2P gossip communication protocol. 

Cassandra also comes with a Log-Structured Merge storage engine. And the engine is consists of some key elements such as-

• Memtable
• SSTables
• Commit Log
• And Compaction 

Just like HBase, now are going to take a glance at the benefits and drawbacks of Cassandra. So that we can easily recognize the winner of the Cassandra vs HBase battle based on our requirements. 

Advantages

• Cassandra comes with flexible schema
• It is also highly-scalable
• Cassandra is highly available and there is no single point failure
• It offers quality write throughput and read throughput 
• It also supports search through secondary indexes 
• Easy NoSQL column family implementation

Drawbacks

• Here is no proper support for ACID properties 
• Cassandra offers no support for aggregates 
• Latency us another headache
• Sometimes JOINS can be an issue
• High chances of data duplication
• And slow reads 

Now we know the pros and cons of both HBase and Cassandra. Now it is the time to discuss which one will be the best choice or the winner of the HBase vs Cassandra battle. At the very beginning, we have mentioned in many ways they look similar like a twin. But after an extent, they have solid differences. We are going to discuss both similarities and differences so that we all can easily pick the winner of the HBase vs Cassandra battle after this blog. 

In order to understand all these better or to make the HBase vs Cassandra battlefield easy to understand, we are presenting the similarities and differences in a table format. So, let’s understand the table-

HBase vs Cassandra: The War begins

We know that after having a glance at the table of similarities and differences, now it’s hard to declare the winner of the Cassandra vs HBase battle. Both seem good options to use. In this way, declaring the best one will be tough. So, let’s know which one to use when so that based on our requirements we can understand the best pick for us. 

According to experts, the HBase and Cassandra use cases can differ from each other based on the application type and outcome expectation.

If you work with a huge batch processing and MapReduce and also you need a good consistency for the large scale reads, choosing the HBase will be the best decision. 

HBase use case examples- online log analytics, support the apps that need a large volume, write-heavy applications, and so on. 

If your task requires the high availability of large scale reads you can use Cassandra. Cassandra also needs a little setup and there is little to zero administration task with stunning flexibility. 

Cassandra use case examples- messaging system development, real-time sensor data management, e-commerce website development. 

So, lastly, we can say that in order to perform aggregations and analyze big data, HBase will be the best option. If your main concerns are interactive data emphasizing and real-time data transactions, using Cassandra will be the ideal choice.