Difference Between Hadoop and Cassandra

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.

Hadoop vs Cassandra

The main difference between Hadoop and Cassandra is that Hadoop is an open-source framework and is limited in its data handling as well as processing capabilities. Cassandra on the other hand is a more sophisticated and highly capable database, designed to provide a structured storing framework across different servers.

Hadoop vs Cassandra

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 Between Hadoop and Cassandra

Parameters of Comparison Hadoop Cassandra
Definition Hadoop is an open-source data handling and processing framework designed by ApacheCassandra 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 dataCassandra 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 provides support for different data types and can store large volumes of data for retrieval later.

The data is stored in the form of clusters in a distributed processing system, where the entire platform spans across the data center.

Thus the data is available from different locations within the data center, 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 as well as 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 center, 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 center 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 it 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 of the network.

Main Differences Between Hadoop and Cassandra

  1. 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
  2. Hadoop is designed to be operated on a single data center. Cassandra is designed to be operated on a distributed data center environment 
  3. Hadoop uses master-slave architecture with hierarchies. Cassandra uses a distributed architecture and provides peer-to-peer communication 
  4. Hadoop can work with structured, unstructured and semi-structured data types. Cassandra also supports structured data types but it cannot work with images
  5. Hadoop works with a 10-15% file compression for handling data. Cassandra works with about 80% file compression for file handling

Conclusion

Data handling is an important aspect of any online or web-based service. Data can be anything, from user information of a social media platform to sales and customer order records of an online business.

As more and more businesses are becoming online, there is an ever-increasing need for quick, efficient, and reliable data handling as well as data managing services.

Both Hadoop and Cassandra are excellent data storage frameworks that provide support for different types of data and offer an efficient, fast, and reliable data storage mechanism that can be easily implemented and scaled according to individual needs.

References

  1. https://ieeexplore.ieee.org/abstract/document/6676732/
  2. https://ieeexplore.ieee.org/abstract/document/7122921/
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