Cassandra vs Relational Databases

Cassandra is a distributed, high-performance, schema-less NoSQL database, with several key differences from traditional relational databases.

1. Data model: Traditional relational databases organize data in tables based on a fixed schema, while Cassandra organizes data in key-value pairs without a fixed schema, allowing for flexible storage of different types of data.
2. Distributed architecture: Cassandra is a distributed database system that can scale horizontally across multiple servers, achieving high availability and high-performance data storage and querying. In contrast, traditional relational databases typically have a single point of failure, limiting performance and availability to the capabilities of a single server.
3. Data consistency: Cassandra ensures data consistency using distributed consistency algorithms, which allow for synchronization and replication of data between different nodes. In contrast, traditional relational databases typically rely on ACID transactions to maintain data consistency, but balancing consistency and performance becomes more complex in a distributed environment.
4. Cassandra uses CQL (Cassandra Query Language) as its query language, which is similar to SQL but with some syntax and functionality differences. Traditional relational databases use SQL as their query language.

In general, Cassandra is suitable for scenarios involving large-scale data storage and high-concurrency queries, as it offers advantages of high performance, high availability, and horizontal scalability. In contrast, traditional relational databases are more appropriate for small-scale data storage and complex transaction processing scenarios.