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.

Generalization in database design serves the purpose of combining similar entities into a higher-level entity, often referred to as a superclass. This helps in simplifying the data model and promoting reusability.

The CHECK constraint is commonly used to enforce data integrity at the column level in a relational database. It allows you to define conditions that must be met for the data in a column, ensuring its correctness.

In handling recursive relationships in database design, you typically use a foreign key within the same table to establish the relationship. This involves referencing the primary key of the same table, allowing an entity to be related to itself. It's a common technique for modeling hierarchical structures.

The concept of slowly changing dimensions (SCD) is primarily concerned with managing changes in dimension attributes over time. SCD techniques help capture historical data, allowing analysts to analyze how dimensions evolve and change over different time periods.

Entity integrity focuses on maintaining the accuracy and uniqueness of attribute values within a single table. It ensures that the primary key values are unique, preventing duplicate or null entries in primary key columns. Referential integrity, on the other hand, maintains consistency between foreign key and referenced primary key values across different tables, ensuring that relationships between tables are valid and accurate.

A Cypher query language is commonly used for graph traversal and manipulation in graph databases. Cypher is specifically designed for querying graph data, making it expressive and efficient for tasks such as pattern matching and graph traversal.

In the context of a retail company's sales data, corresponding dimension tables would likely include information about customers, products, and time. This allows for analysis based on customer preferences, product performance, and temporal trends.

In column-family stores, data is organized into families. A column family is a container for rows that share a similar structure. It is a way of grouping related data together, providing a flexible schema where each row in the family doesn't have to conform to the same set of columns.