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