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.

Database design tools, in addition to ERD design, often include features for report generation. These tools enable users to create detailed reports based on the data model, providing valuable insights into the structure and relationships within the database. This aids in better understanding and communication of the database design.

Clustering in the context of databases refers to a technique for organizing data into groups (clusters) based on similarity. This helps in organizing data efficiently and can improve query performance by reducing the amount of data that needs to be processed.

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.