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The choice of HBase as the correct answer makes sense given its capabilities in handling structured and semi-structured data. HBase is designed to provide real-time read/write access to large datasets and is built on top of the Hadoop ecosystem. It efficiently stores data in a sparse table format, which allows it to accommodate both structured and semi-structured data types effectively.

Moreover, HBase operates on a schema-less design at the column family level, enabling users to store varied types of data without stringent constraints on data types or structures, making it quite suitable for diverse applications. Although HBase does support transactions to some degree, it is less robust compared to traditional relational databases, which aligns with the requirement of having limited support for strong transactions.

In contrast, the other options focus on different types of data handling and architectures. For instance, while MongoDB is excellent for semi-structured data and known for its flexible document model, it also provides stronger transaction support than HBase, particularly with multi-document ACID transactions. Redis, primarily an in-memory data structure store, excels in speed and real-time analytics but does not naturally support large datasets like HBase does. Meanwhile, Cassandra is designed for high availability and horizontal scalability, offering better support for wide-column storage