Cassandra vs MongoDB

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Cassandra
Cassandra

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MongoDB
MongoDB

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Cassandra vs MongoDB: What are the differences?

Developers describe Cassandra as "A partitioned row store. Rows are organized into tables with a required primary key". Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL. On the other hand, MongoDB is detailed as "The database for giant ideas". MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.

Cassandra and MongoDB can be primarily classified as "Databases" tools.

"Distributed", "High performance" and "High availability" are the key factors why developers consider Cassandra; whereas "Document-oriented storage", "No sql" and "Ease of use" are the primary reasons why MongoDB is favored.

Cassandra and MongoDB are both open source tools. It seems that MongoDB with 16.2K GitHub stars and 4.08K forks on GitHub has more adoption than Cassandra with 5.23K GitHub stars and 2.33K GitHub forks.

According to the StackShare community, MongoDB has a broader approval, being mentioned in 2175 company stacks & 2143 developers stacks; compared to Cassandra, which is listed in 337 company stacks and 231 developer stacks.

What is Cassandra?

Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.

What is MongoDB?

MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.
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