Handling big amounts of data is not easy, as only a small mistake in the process of storing the data can lead to the entire data being corrupted or even lost.
Hence, the data platforms need to be sophisticated as well as well equipped for handling storage of, as well as operations on such large datasets.
Key Takeaways
- Hadoop excels in batch processing and handling large volumes of structured and unstructured data, while Cassandra is designed for real-time, high-availability, and high-write-load scenarios.
- Hadoop relies on HDFS for data storage, providing fault tolerance and data replication, while Cassandra uses a distributed and decentralized storage model.
- Hadoop’s ecosystem includes tools like MapReduce, Hive, and Pig, while Cassandra’s CQL language offers SQL-like capabilities for querying.
Hadoop vs Cassandra
Hadoop is a data processing framework that enables distributed storage and processing of large data sets across clusters of computers. Cassandra is a distributed NoSQL database management system that uses a peer-to-peer architecture to ensure high availability and fault tolerance.
Hadoop is a data-storing framework designed by Apache. The software is built on Java and provides the essential data storage as well as operational functions required while handling large datasets.
It is an open-source framework that is designed for deployment over low-cost and primitive hardware. Hadoop allows a single file to be stored in multiple nodes.
Cassandra is a highly capable and sophisticated data storage platform developed by Apache. It is designed to be deployed over a distributed server network.
Thus it provides a single data storage framework for a large server network, where files are stored as nodes in a cluster accessible from different servers.
Comparison Table
| Parameters of Comparison | Hadoop | Cassandra |
|---|---|---|
| Definition | Hadoop is an open-source data handling and processing framework designed by Apache | Cassandra is a highly sophisticated and highly scalable data handling framework designed to store large datasets |
| Operation | It is designed to be operated on a single data center | It is designed to be operated on a distributed data center environment |
| Architecture | Hadoop uses a master-slave architecture with hierarchies | Cassandra uses a distributed architecture and provides peer-to-peer communication |
| Data types | Hadoop can work with structured, unstructured, and semi-structured data types | Cassandra also supports structured data types but it cannot work with images |
| File compression | Hadoop works with a 10-15% file compression for handling data | Cassandra works with about 80% file compression for file handling |
What is Hadoop?
Hadoop is an open-source framework designed by Apache for storing and handling big data. It supports different data types and can store large volumes of data for later retrieval.
The data is stored in the form of clusters in a distributed processing system, where the entire platform spans across the data centre.
Thus the data is available from different locations within the data centre, provided the servers are located in one geographical location.
Hadoop uses Master-Slave architecture for storing data, and thus a hierarchy is followed to maintain clean and efficient storage. Hadoop provides support for structured, unstructured, and semi-structured data types, including images.
The platform functions according to the MapReduce programming model, which is best suited for handling large volumes of data. The program functions by creating a cluster of nodes and distributing the data across the nodes.
Thus as the nodes are available from different locations within the data centre, it increases the availability and retrieval of data. The file system used for managing data in this format is known as the Hadoop Distributed File System (HDFS).
10-15% compression is used to store data. This allows for a quicker experience as compared to the traditional database approach.
The scalability offered by Hadoop is also much higher than the traditional databases, increasing the capability of Hadoop for storing huge datasets.
What is Cassandra?
Cassandra is a highly capable and sophisticated data storage framework designed by Apache. It is a NoSQL database and is designed to provide high-speed data storage functions with increased availability of files.
It is a distributed data storage framework and is meant to be deployed over a large server network. The files are thus available for different servers in the data centre, and retrieval of the stored data is possible from all the servers.
The design of the Cassandra framework is based on the Dynamo framework from Amazon, and it uses the same NoSQL format.
This allows the framework to store large volumes of data in a distributed network, accessible from anywhere within the server network.
Cassandra supports structured, unstructured, and semi-structured data sets but does not support image files. Hence image files cannot be stored using the framework.
The best feature of Cassandra is its scalability. It uses a distributed architecture and provides peer-to-peer communication. This increases the scalability of storage and also the speed of the entire process.
The data is stored in nodes within a cluster. The nodes can be read or written from within the cluster, and as it is in a distributed environment, the process can be performed from any machine in the network.
Main Differences Between Hadoop and Cassandra
- Hadoop is an open-source data handling and processing framework designed by Apache. Cassandra is a highly sophisticated and scalable data-handling framework that stores large datasets.
- Hadoop is designed to be operated on a single data centre. Cassandra is designed to be operated in a distributed data centre environment.
- Hadoop uses master-slave architecture with hierarchies. Cassandra uses a distributed architecture and provides peer-to-peer communication.
- Hadoop can work with structured, unstructured and semi-structured data types. Cassandra also supports structured data types but cannot work with images.
- Hadoop works with 10-15% file compression for handling data. Cassandra works with about 80% file compression for file handling.
- https://ieeexplore.ieee.org/abstract/document/6676732/
- https://ieeexplore.ieee.org/abstract/document/7122921/
Last Updated : 22 June, 2023
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Sandeep Bhandari holds a Bachelor of Engineering in Computers from Thapar University (2006). He has 20 years of experience in the technology field. He has a keen interest in various technical fields, including database systems, computer networks, and programming. You can read more about him on his bio page.
This comparison misses the mark. Hadoop and Cassandra have much more in common than highlighted here. I believe a deeper analysis is warranted.
I agree with you, Bennett. This comparison only scratches the surface. There’s a lot more to consider when choosing between Hadoop and Cassandra.
This article is very comprehensive and well-researched. The comparison table makes it easy to understand the differences between Hadoop and Cassandra. Great piece!
The comparison was very enlightening. It looks like both systems are ideal for different purposes. Hadoop for batch processing and Cassandra for real-time data. This is very informative.
The detailed explanations of both Hadoop and Cassandra are quite impressive. I find the emphasis on their differences very helpful for understanding their unique capabilities. Excellent work!
I appreciate the attention to detail in explaining the architecture and operations of both Hadoop and Cassandra. It’s clear that both have their advantages and it’s important to choose the right one based on specific data requirements.
The author does a great job at simplifying complex concepts. I didn’t know about the 80% file compression used by Cassandra. Thank you for sharing this valuable information.