What are the differences between Cassandra and traditional relational databases?

Cassandra is a distributed database management system, while traditional relational databases are typically single-point databases. Here are a few differences between Cassandra and traditional relational databases:

1. Data Model: Cassandra utilizes a distributed NoSQL data model, storing data across one or multiple nodes and managing large-scale data through partitioning and replication. In contrast, traditional relational databases organize data using tabular structures and require defining relationships between tables.
2. Scalability: Cassandra can scale horizontally by adding nodes to handle larger amounts of data. Traditional relational databases typically scale vertically by increasing the processing power of servers to improve performance.
3. Data consistency: Cassandra ensures eventual consistency, meaning at a specific time point, data may have different views, but will eventually reach a consistent state. Traditional relational databases typically guarantee strong consistency, where all replicas have the same data view at the same time point.
4. Data processing: Cassandra utilizes a distributed query language (CQL) for handling data, enabling support for complex queries and analysis. Traditional relational databases use SQL language for querying and manipulating data.

In general, Cassandra is suitable for scenarios that require handling large-scale data and high availability, while traditional relational databases are suitable for scenarios that require strong consistency and transaction support.