Which feature of Cassandra allows for handling partitioned data efficiently?

The correct option highlights the distributed architecture of Cassandra, which is fundamental to its capability to handle partitioned data efficiently. In a distributed architecture, data is distributed across multiple nodes in a cluster, allowing for horizontal scalability and improved data locality. This means that data can be partitioned based on a key, and each partition can live on a different node. As a result, queries can be executed in parallel across several nodes, significantly enhancing performance and response times, particularly for large datasets.

This architecture also enables fault tolerance, as data replication is spread across various nodes, ensuring that even if one or more nodes fail, data remains accessible. Additionally, it optimizes load balancing by distributing read and write requests across the nodes, thereby eliminating potential bottlenecks that might occur with a centralized system.

The other features listed, while useful in their own contexts, do not primarily address the efficiency of handling partitioned data in the way that a distributed architecture does. Materialized views are used for query optimization and improving read performance, triggers allow for executing custom logic on data changes, and compaction relates to data storage efficiency. None of these features directly contribute to the fundamental ability of Cassandra to efficiently manage and scale partitioned data across a distributed system.