What does the Data Modeling process help accommodate in Cassandra?

The Data Modeling process in Cassandra is fundamentally designed to address scalability and performance needs. In a distributed database like Cassandra, the way data is modeled directly influences how efficiently it can be stored, retrieved, and queried.

Cassandra's architecture is optimized for horizontal scalability, meaning that as the amount of data increases or the number of requests grows, the system can seamlessly expand by adding more nodes. Proper data modeling takes advantage of partitioning, clustering, and replication strategies inherent in Cassandra. By organizing data in a way that aligns with access patterns, applications can achieve low-latency responses and high throughput, which are crucial for performance in distributed environments.

These considerations ensure that the system can maintain efficiency as it grows and that it can handle large volumes of concurrent queries without degradation of performance. This focus on scalability and performance is what distinguishes effective data models in Cassandra and guides database design decisions tailored to specific application requirements.

The other options, while relevant in the context of database systems or application design, do not capture the primary goal of data modeling in Cassandra, which is explicitly oriented toward optimizing how data is structured and accessed for scaling and high performance.