Graph databases typically handle queries involving traversing relationships using specialized graph query languages like Cypher. Cypher is designed to express patterns and relationships in the graph, making it more intuitive and efficient for querying graph databases compared to traditional SQL queries.

Strategies for handling data partitioning in distributed Key-Value Stores include Consistent Hashing, Range Partitioning, and Composite Key Partitioning. These techniques help distribute data evenly across nodes, ensuring efficient data retrieval and storage in distributed environments.

In column-family stores, data partitioning is typically based on row keys. Rows with similar or related keys are stored together, allowing for efficient retrieval of data within a specific partition. This enhances performance by minimizing the need to scan the entire database for relevant records.

A Relational Database would be the best fit for this scenario. Relational databases provide strong support for complex queries, transactions, and aggregations, making them suitable for handling the structured and transactional nature of financial data.

Challenges in implementing a Generalization and Specialization hierarchy include increased complexity in querying and retrieval. While it provides a structured hierarchy, the intricacies may pose challenges in certain operations.

Query caching plays a crucial role in database performance tuning by storing the results of frequently executed queries in memory. This reduces the need to re-execute identical queries, thereby improving response time and overall system performance. However, it is essential to manage query caching effectively to ensure that cached data remains consistent and up-to-date.

A Primary Key uniquely identifies a record within a table. It ensures that each row in the table is unique, and it serves as the basis for establishing relationships with other tables.

Each row in a column-family store is identified by a unique key. This key is used to retrieve and distinguish the row from others in the same family. The key is crucial for efficient data retrieval in column-family stores.

For a recommendation system based on interconnected data, a Graph Database would be most suitable. Graph databases excel at representing and querying relationships between entities, making them ideal for scenarios where the connections between data points are crucial, such as social networks.

Sharding is a technique used to improve query performance by limiting the partitions that need to be scanned. It involves dividing a large database into smaller, more manageable parts called shards, which can be processed independently.