Which factor influences application performance and scalability in a system using Cassandra?

Data modeling is critical in influencing application performance and scalability in a system using Cassandra. Proper data modeling allows for efficient data retrieval, optimized queries, and minimized read and write latency. Since Cassandra is a distributed NoSQL database designed to handle large amounts of data across many commodity servers, how data is structured — including how tables and partitions are created — directly impacts how well the system performs under load.

Effective data modeling in Cassandra involves understanding how data will be queried. By designing the data model to match query patterns, developers can leverage the database’s partitioning and replication strategies to ensure that read and write operations are distributed evenly across the nodes. This not only enhances performance by reducing hotspots but also facilitates scalability since the database can handle increased amounts of data by simply adding more nodes without the need for a complete redesign of the data model.

User access management, network bandwidth, and data backup frequency can all play roles in the overall system performance and reliability, but they don't directly impact how efficiently the database handles data or how scalable it is in the way that data modeling does. For instance, while network bandwidth is important, it is often related to the infrastructure capabilities rather than the structure and design of the data itself.