Cassandra Vs Hadoop: Difference Between Cassandra and Hadoop

Big Data is thriving, and so are the technologies associated with it. Cassandra and Hadoop are a few of the popular technologies, which are used for, in simple terms, data analysis. Though there are many overlapping factors, the key differences between the two have been discussed below. These factors will help you select the best one based on your needs. 

Difference Between Cassandra and Hadoop 

Fundamental Difference

Hadoop is a Big Data processing framework, while Cassandra is the distributed NoSQL database made to process a large amount of data. They may appear similar, but they are two different entities serving different purposes.


While Hadoop goes with batch processing, Cassandra is famous for real-time processing. Furthermore, both are PRO in the analysis of data generated through the online mode, like mobile or web, and can address online requests instantly. 

Must Read: Hadoop Tutorial for Beginners

Consistency, Availability, and Partition Tolerance (CAP)

Hadoop focuses on CP, i.e., Consistency and Partition Tolerance while Cassandra follows AP or Availability with Partition Tolerance. 

Formats Supported

Casandra and Hadoop support all the formats, i.e., structured, semi-structured, unstructured, and images except, Cassandra does not support images.

Read: Top Hadoop Project Ideas & Topics


The key difference lies in architecture, which affects the performance and speed. While Hadoop is famous for master-slave architectural design (Name Node is the master and Data Node is the slave), Cassandra works on distributed architectural design. In the cluster, each node has the same role, unlike Hadoop, and the communication among those is in a peer-to-peer fashion.

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Tolerance to Fault

As mentioned prior, architectural design is highly responsible for the performance, and so are faults and failures. Cassandra is always the first choice if the probability of fault occurrence is required to be low. In master-slave design, a slight fault can bring down the whole system while in distributed design, other nodes will take care of all the requests.

Data Compression and Protection

At most, Hadoop can compress the data up to 15% while Cassandra can compress it up to 80%. That is a lot of compression at no cost! 

If we draw our attention to data protection, then both technologies are best in their ways. While Hadoop provides audit and access control, Cassandra has a commit log design that provides functions like backup and restores.

Data Flow and Storage Model

Hadoop data is directly written in the data note, whereas Cassandra is first written in memory, and then in the disk. It is written in the memory structure format, which is also called mem-table

Considering the storage model for Hadoop the term Hadoop Distributed File System or HDFS is coined where huge files are broken and replicated in many nodes. A different strategy is followed in Cassandra. The Keys Space Column strategy is followed, where primary and secondary indexing is done.

Learn about: Top Hadoop Tools

Logical Data Model

If we talk about the logical data model of Cassandra and Hadoop (refer to the images), we will find that in Hadoop, data is partitioned by a 1-column row key while in Cassandra data is partitioned by a multi-column primary key. It has been found that the data logic arrangement in Cassandra is more convenient as compared to the lexicographical order followed by Hadoop. 

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Replication Factor

Replication factors are the unit that defines the number of data replicas that have been stored on multiple nodes to ensure fault tolerance and reliability. For Hadoop, the replication factor is constant (3 by default); however, in Cassandra, it is the number of nodes in the data centre.


Data is stored in key-value pair fashion, which makes indexing very simple in Cassandra as compared to Hadoop. 

Cassandra Vs. Hadoop: Comparison Table

In this, Cassandra vs Hadoop section, get a clear and concise understanding of the key differences between Cassandra and Hadoop. 

Hadoop Cassandra
It is a scalable framework that can be installed on inexpensive hardware. It is set up as a cluster of connected instances that are deployed in a fairly distributed manner.
Hadoop supports different file formats. Cassandra does not support images.
A single data center hosts Hadoop’s deployment. Cassandra’s deployment is quite scattered.
One can write data directly to the data node, thanks to Hadoop. Data has been written to the disk after it has been stored in the mem-table in Cassandra.
High latency rate Low latency rate
Ideal for data batch processing. Ideal for real-time processing.
The popular MapReduce programming approach is the foundation for Hadoop, a big data processing platform. Real-time data processing is the primary application of Cassandra.
The architecture of Hadoop is master-slave. The architecture of Cassandra is peer-to-peer.
It read/wrote using map-reduce. It leverages the Cassandra query language.
Hadoop uses a replication factor that remains unchanged at 3. The Cassandra replication factor relies on the node count.
Leverages UDP and TCP for communication. Leverages gossip protocol for communication
Multiple index creation in Hadoop is challenging. Quickly create multiple indexes as Cassandra stores data as key-value pairs.

Cassandra Vs. Hadoop: Use Cases

Cassandra and Hadoop each offer advantages and disadvantages, as we saw in the previous section. But how do those qualities work in practice? What kind of circumstances call for the usage of one over the other? 

Cassandra is perhaps more suitable for: 

  • Data processing in real-time 
  • Online transactional data and apps that are constantly active and swift 
  • Managing an abundance of simultaneous traffic and engagement.
  • Circumstances in which only a small quantity of data is processed during each interaction 
  • Programs demanding a high level of large-scale read availability 

Note: When it comes to HDFS vs Cassandra, HDFS lacks record-level indexing. So, structured data must be stored in a data warehouse. 

Not that it’s the best fit for the application, but occasionally HBase is implemented for a web-based application since a Hadoop deployment already exists at a site. 

HBase is often not an ideal choice for creating perpetually active web applications and is behind Cassandra by nearly two to three years in many technological domains. Learn more about this in a separate comparison of Cassandra vs MongoDB vs HBase

Here are just a few examples of the numerous applications for Cassandra: 

  • Online shopping and inventory control 
  • Customer experience, recommendations, and personalization 
  • Edge computing and the Internet of Things 
  • Authentication and fraud detection 

Hadoop provides enterprises significant value by enabling the efficient processing and analysis of enormous amounts of data. It is common practice to use that analysis to guide crucial decision-making. In light of this, you want to think about using Hadoop for the following: 

  • Conducting real-time, big data analytics on historical data
  • processing and storing huge data volumes in batches 
  • Data warehousing and data lakes 

Here are some examples of applications for Hadoop and its elements: 

  • Shopping analytics 
  • Trading, analysis, and forecasting of financial risks 
  • Uses in the field of medicine, such as identifying patients at risk for serious illness 
  • Social networking services with extremely high volume processing, like Twitter and Facebook

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What Next?

With almost similar processing and other attributes, there is always confusion while choosing the ‘best’ out of Cassandra and Hadoop. There have been instances where technology leaders claim that Cassandra delivers more as compared to Hadoop like in the case of architecture; it has an easier setup and less requirement along with an easier and flexible development environment. Nevertheless, Cassandra lacks data consistency. 

The best choice depends on the requirement, as there is no tug-of-war of Cassandra vs Hadoop. For example, if performance is the major focus, then Cassandra is the best option, as it provides high availability, scalability, and low latency. It works wonders with real-time data analysis, unlike Hadoop.

Hadoop, on the other hand, is suggested when voluminous data needs to be searched, reported, stored, or analysed. As Big Data grows, so does the functionality of each technology. It depends on us how wisely we use it. 

It has been rightly said that data is the fuel and it will drive technology and gradually the whole world. Small enterprises or giant organisations both deal with data. From data elicitation to processing, each step requires predictive analysis skills and strong fundamental knowledge. This knowledge will not only help you grow professionally but also increase the probability of career success. 

upGrad has started online courses with certification in Big Data. Courses like Artificial Intelligence, Big Data, and Data Science are already on the hit list. There have been more than 4000 students across the globe who have started or completed the Big Data course.

If you are interested to know more about Big Data, check out our Advanced Certificate Programme in Big Data from IIIT Bangalore.

Knowing that knowledge leads to actual power, you cannot afford to waste time in this pandemic. Imparting knowledge with implementation and hands-on experience is what you get at upGrad. You will not just get the theoretical knowledge of Cassandra and Hadoop but their application too.

And this is not the end; you get placement assistance along with regular interaction with your trainers and classmates. The career counselors at upGrad will help you choose the most suitable for your profile and skill set. So, what are you waiting for? 

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What are the factors that affect Hadoop MapReduce’s performance?

The performance of Hadoop is affected due to the processing time of data. The processing time undergoes an impact due to several reasons. One of them is the algorithm behind the separate functions, namely, Map and Reduce. Other factors contributing to poor performance are hardware such as memory size, CPU, disk space, etc. Another factor is the issue with the storage system, which makes it often difficult to keep up with terabytes of data. The data size for different kinds of data, such as input data, output data, etc., impacts the runtime of a job.

How does Cassandra stand out?

Cassandra is the first choice for many businesses, organisations, and institutions when choosing a NoSQL database. The primary reason is the feasibility and availability of its services. There is barely any room for failure. Since Cassandra operates with no single point of failure, it becomes an optimal choice for businesses. Moreover, there is consistent access and availability throughout the process. Another reason why Cassandra is unique is its ability to work with voluminous data. It can flawlessly bite through petabytes of data and handle its service for as long as you need it. Also, it is very effective in performing writing operations over a humongous amount of data across servers without putting any stress on the read efficiency.

Who is Cassandra optimal for?

Due to the gallons of advantages that Cassandra offers, the market demand for the technology is experiencing an efflux. However, Cassandra is your saviour if your business needs to store data in massive amounts across servers. Businesses and organisations that cannot function without data can opt for Cassandra. Furthermore, companies that have a zero tolerance policy against database outages due to a single server can also consider Cassandra their ideal solution. Businesses that are constantly keeping up with the market demands can use this technology to upscale themselves since Cassandra is highly scalable and easy to use.

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