What are partitioning strategies in Cassandra used for?

Partitioning strategies in Cassandra are critical for determining how data is distributed across the nodes in a cluster. This is essential for ensuring that data can be accessed quickly and efficiently. By employing specific partitioning strategies, Cassandra can evenly distribute data among all the nodes, allowing for balanced workloads and optimal read/write performance.

Considering the nature of distributed databases, effective partitioning helps to minimize hotspots and ensures that no single node becomes a bottleneck. It also plays a pivotal role in scalability, as a well-chosen partitioning strategy enables the system to effectively handle larger datasets as more nodes are added.

While factors like redundancy levels, user access, and visual representation of data matter in a database operation, they are not the primary concern of partitioning strategies. The focus of partitioning is inherently on the distribution of data across nodes, making option B the precise choice that reflects the core purpose of partitioning strategies in Cassandra.