Cassandra is a Better Option for Handling Big Data in a No-SQL Database

Author

Jyothi J

Abstract

With no single point of failure and the flexibility to handle enormous volumes of data across numerous commodity servers, Apache Cassandra is an open-source distributed database management system. With asynchronous master less replication, Cassandra provides strong support for clusters spanning several datacenters and enables low latency operations for all clients. Like skilled carpenters, data engineers are aware that certain tasks call for various tools. Choosing the appropriate equipment and being knowledgeable about their use can be the most crucial aspects of any job. A distributed information system for managing massive amounts of structured data across multiple goods servers, Apache Cassandra, a top-level Apache project developed at Facebook and based on Google's Big Table and Amazon's Generator, offers extremely offered service and has no single point of failure. In comparison to other NoSQL databases and relational databases, Cassandra offers features including continuous availability, linear scale performance, operational simplicity, and straightforward knowledge distribution over various knowledge centers and cloud availability zones. Cassandra's capacity to scale, perform, and supply continuous time period is due to its design. Cassandra uses an attractive, simple-to-setup, and simple-to-maintain lordless "ring" design rather than a traditional master-slave or a manual and labor-intensive shared system. With continuous availability, linear scale performance, operational simplicity, and simple data distribution over numerous data centers and cloud availability zones, Apache Cassandra is a massively scalable open-source non-relational database. Cassandra was initially created at Facebook; it was open sourced in 2008; and in 2010, it was elevated to the status of a top-level Apache project

Keywords

NoSQL database, Cassandra, Column Database, Big Data, Big Data Analytics, RDBMS, and NoSQL

DOI : https://doi.org/10.55248/gengpi.2022.3.9.27

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References

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