Cassandra Practice Test

Revisiting a data model in Cassandra due to imbalanced data is significant because data distribution directly impacts performance and efficiency. Cassandra is built to handle large volumes of data across multiple nodes, and an imbalanced dataset can lead to hotspots where some nodes become overloaded while others are underutilized. This imbalance can cause delays in read and write operations, increased latency, and potential bottlenecks, ultimately leading to poor application performance.

To address this, analyzing and potentially redesigning the data model ensures an even distribution of data across the cluster. By revisiting the model, you can optimize how data is partitioned and replicated, thereby improving scalability and reliability as the dataset grows or as application usage patterns change.

While other reasons listed might prompt a review of the database or application as a whole, imbalanced data specifically targets the foundational aspect of Cassandra's architecture, necessitating a focus on optimizing the data model for improved cluster performance.