HBase vs. Cassandra: Difference Between HBase and Cassandra [2023]


While working with large datasets, it is crucial to have storage units and management systems that can handle such a vast amount of data. HBase and Cassandra are two such scalable databases. Both of them are capable of handling large amounts of data without compromising performance. Both of them are open-source databases and work on the concept of linear scalability. Additionally, both of the databases are non-relational. 

Cassandra is used by companies like Activision, Apple, Instagram, and Netflix, to name a few. 

HBase is used by companies like Siemens, Nvidia, and Pinterest, to name a few. Some of the implementations of Cassandra at Apple handles over 10PB of data. 

In our analysis of HBase vs. Cassandra, we discuss the differences and some shared functionalities between these systems. It would help you decide which database to use in your next big data project and help in bridging some of the knowledge gaps regarding these databases. 


1. Base infrastructures 

While discussing HBase vs. Cassandra, it is first necessary to understand the base infrastructures. Although both are said to be on NoSQL databases, Cassandra is more of a pure implementation. Unlike Cassandra, HBase uses the NoSQL database developed on top of Hadoop. Moreover, Cassandra and HBase were both derived from Google’s BigTable, but in HBase, we find some traces of Amazon’s Dynamo.

2. Base nodes

In HBase, we use master nodes used to monitor and coordinate the local servers and the actions associated. Along with the master nodes, there are standby nodes. These are used in case a master node fails and also in inter-node communications. While in Cassandra, there are seed nodes rather than master nodes, for inter-node communications. To ensure the scalability and performance, in Cassandra, a single cluster contains multiple seed nodes. 

3. Internode communication 

In either of the databases, inter-node communication is crucial for driving the database’s performance on large datasets. In HBase, ZooKeeper protocol handles all the inter-node communications. This protocol is quite similar to a master-slave network protocol. In the ZooKeeper protocol, one node acts as the master, and other nodes are dependent on the master node for all the communication.

In Cassandra, a Gossip protocol is used. Just like the word suggests, communication is chained. The inter-node communication is implemented through message passing between the nodes. So, at each node, the data is replicated. 

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4. Query language

Although HBase and Cassandra are both based on JRuby and Shell, there is one critical point for HBase vs. Cassandra. Cassandra uses its specific query language known as CQL. Cassandra Query language or CQL is the primary language to communicate with a Cassandra database. To access Cassandra through CQL, the most common tool used is CQL Shell (CQLSH). Due to this, sometimes Cassandra is considered more feature-rich than HBase. 

5. Transaction mechanisms 

As both are non-relational databases, Cassandra and HBase do not offer the basic RDBMS ACID transactions using rollback and locks. Cassandra provides atomic, isolated, and durable transaction mechanisms with tunable consistency.

In HBase, a transaction server is used to handle transactions. For lightweight transactions, HBase uses Read-Check-Delete and Check-Put means. Cassandra, however, uses Compare-Set and Row-Write-Isolation for handling lightweight transactions.

6. Infrastructure

As discussed earlier, HBase is developed upon Hadoop. Hence, HBase uses the base infrastructure of Hadoop and provides similar functionalities like ZooKeeper, master nodes, etc. However, Cassandra is built using protocols such as Gossip, Snitch, Partitioner, Replica placement strategy, etc.

Cassandra uses elements like datacentres and string sorting and CQL tables. Cassandra is often used along with Hadoop or Storm, by keeping the infrastructure type as a single-node. There is only one drawback to doing so. The complexity increases with the usage of other systems in combination with Cassandra. 

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7. Partitioning and coprocessor 

HBase does not provide support for ordered partitioning. Although HBase allows for coprocessor functionality, it fails to support read-load balancing as a single row is considered one region server. Cassandra, however, provides support for ordered partitioning. With this, Cassandra can offer a row size of several MBs. But Cassandra neither supports the range-based row scans, nor it provides support for coprocessor functionality.  

These were some of the core differences in HBase vs. Cassandra. But few other differences should be looked at too.  

Bloom filters: A bloom filter is a data structure used for processing continuous streams of data. Bloom filters are efficient in terms of space and are probabilistic. HBase uses boom filters to check whether a particular row or cell is there in the StoreFile or not. Some consider boom filters as a sort of indexing for HBase. In Cassandra, boom filters are used in searching for some data in a particular file. 

Setup and documentation: Although HBase may seem easier to set up, the Cassandra cluster is much easier to tune and set up than the HBase cluster. Cassandra also offers somewhat better documentation that makes it easier for adapting and learning. HBase workings could be trickier to understand. Nevertheless, with proper sources, can be known effectively. 

Usage over WAN: Both the databases allows for data replication functionality over WAN (Wide Area Network). In Cassandra, it is done through row replications provided by a random partitioning module. In HBase, the reproduction is performed asynchronously by replicating the clusters as storage units. 

Case Studies and Real-world Examples

To truly understand the difference between HBase and Cassandra, it’s essential to delve into real-world use cases and examine how these technologies have been implemented successfully. Let’s explore a few compelling case studies that highlight the strengths and applications of each database system.

HBase’s adoption by major social media sites to manage enormous volumes of data is one prominent illustration of its efficacy. HBase is used by social media businesses such as Facebook and Twitter to store and handle massive amounts of user-generated material in real-time. Because of its capacity to give low-latency access to vast datasets, it is an excellent solution for dealing with the massive data input created by social media users.

On the other hand, Cassandra has found significant adoption in industries that require high availability and fault tolerance. For instance, Netflix, the renowned streaming service, relies on Cassandra to power its robust and resilient backend infrastructure. Cassandra’s distributed architecture allows Netflix to scale horizontally and handle millions of concurrent requests seamlessly, ensuring uninterrupted streaming experiences for its global user base.

These case studies illustrate how HBase and Cassandra have been successfully employed in different domains, showcasing their unique capabilities and advantages in specific contexts.

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Future Trends and Evolving Features

As technology advances and new demands emerge, database systems like HBase and Cassandra continuously evolve to address these challenges. Let’s take a glimpse into the future and explore some of the exciting trends and evolving features in these databases.

In terms of HBase, one prominent trend is the integration of HBase with other Apache big data projects. For instance, the integration with Apache Spark enables HBase to leverage Spark’s distributed computing capabilities for advanced analytics and machine learning tasks. This integration opens up new possibilities for leveraging the power of both technologies, providing users with enhanced data processing and analysis capabilities.

Furthermore, HBase is actively exploring ways to optimize its storage efficiency and reduce its storage footprint. With the increasing volume of data being generated, efficient storage utilization becomes crucial. Ongoing research and development in this area aim to improve compression techniques and introduce innovative storage mechanisms that minimize resource requirements while maintaining high performance.

Similarly, Cassandra is also evolving to meet the demands of modern data management. One notable trend is the focus on multi-model capabilities, allowing Cassandra to support different data models within the same database. This provides developers with more flexibility in handling diverse data types, such as graph data or time series data, within a single system.

Another key area of development for Cassandra is enhanced security features. With data breaches becoming a significant concern, Cassandra is investing in strengthening its security mechanisms, including improved authentication and authorization controls, encryption at rest and in transit, and auditing capabilities. These enhancements aim to provide users with a robust and secure data storage solution.

Also Read: Hadoop Tutorial: Ultimate Guide to Learn Big Data Hadoop


So, these were some of the primary points of our analysis of HBase vs. Cassandra. As both the databases are based on NoSQL, they are sometimes confused as twins. But while looking at the core complexities, the differences surface. By looking at the analysis, selecting the useful one for your big data project is possible. Both databases have some merits and demerits. Hence, it is crucial to understand the usage of the database in your project and select accordingly. 

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What is CQLSH?

CQLSH stands for Cassandra Query Language Shell. It is an environment through which the users can create schemas, insert data, and execute queries. Like Structured Query Language (SQL), it also has data definition, data manipulation commands, and clauses. The data definition commands are: create, alter, drop, etc. The data manipulation commands are: insert, update, delete, and batch. Clauses include select, where, and order by. It is implemented using Python native driver protocol. It has specialised commands for tracing queries, capture for writing out the output to a file, etc.

What is Hadoop?

Hadoop is a Big Data processing tool that helps store data and performs operations on it. Due to the wide variety of data being generated each day, storage techniques should be optimal. Hadoop is fast, scalable, resilient, and cost-effective. Hadoop uses a Hadoop Distributed File System to store and manage data efficiently and is based on master-slave architecture. For processing data quickly, it uses MapReduce, which processes the data based on grouping, sorting, and frequency. Hadoop has a vast ecosystem, thus allowing it to work with various tech stacks and tools. It includes YARN for resource management, Pig for scripting, Spark as a dataflow engine, etc.

What are Rollbacks and Locks?

Rollback is a process of undoing all the changes made in the transaction, freeing the resources, and bringing them back to the initial state. Rollbacks have to be done in case of transactions where different transactions depend on each other in terms of data and execution order. Locks are used to ensure serialisability and recoverability. There are two types of locks: shared lock and exclusive lock, also known as the read-and-write lock. If a data item has a lock to it, no other data transaction can access it until the lock on that data item is released. Rollbacks and locks are needed to prevent inconsistencies and ensure that a transaction's ACID properties are satisfied.

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