The CAP theorem in NoSQL databases states that it is impossible to achieve all three of Consistency, Availability, and Partition Tolerance simultaneously. Understanding this theorem is crucial in making design decisions related to data consistency in distributed systems.

Data Denormalization is the process of organizing data in a way that minimizes data movement and maximizes storage utilization. In contrast to normalization, denormalization involves combining tables and introducing redundancy to improve query performance by reducing the number of joins required.

In this context, applying Generalization and Specialization would involve considering adjuncts as a subtype of professors. This allows for shared attributes and behaviors among professors and adjuncts while maintaining distinct characteristics for each faculty role.

Clustering algorithms can be used to group users based on their similar interests, preferences, and behavior patterns. By clustering users with similar interests together, the social media platform can recommend relevant content to each user based on the preferences of their respective clusters, thereby enhancing user experience.

The eventual consistency model in NoSQL databases prioritizes availability over consistency. It allows for temporary inconsistencies between distributed nodes, aiming to provide high availability and fault tolerance in scenarios where immediate consistency is not crucial.

Data partitioning is employed to enhance performance in distributed systems. It involves dividing large datasets into smaller, more manageable partitions, distributed across multiple nodes. This aids in parallel processing and efficient utilization of resources, ultimately boosting system performance.

Type 2 SCD maintains historical data by adding historical columns to the dimension table. This includes fields like start date, end date, or version number to track changes over time and preserve historical information about the dimension's attributes.

In database modeling, a superclass is a high-level table that shares common attributes with one or more lower-level tables, known as subclasses. It allows for the representation of generalization and specialization relationships in a database.

The primary query language used in document-based databases is BSON (Binary JSON). BSON is a binary representation of JSON-like documents and is the format used to store and query data in databases like MongoDB. It allows for complex queries and efficient storage of diverse data types.

Version control features in ER diagram tools allow the team to automatically track changes, providing a history of modifications. This enables the team to manage the evolution of the database schema, understand who made specific changes, and roll back to previous versions if needed. It ensures collaboration without the risk of conflicting changes.

In an ERD, superclasses are represented by diamonds, and subclasses by rectangles. The diamond symbolizes the generalization relationship, illustrating that the subclass inherits attributes from the superclass.

The most common schema used in Dimensional Modeling is the Star Schema. In a Star Schema, a central fact table is connected to multiple dimension tables, forming a shape resembling a star. This design simplifies queries for analytical reporting and allows for easy navigation between dimensions and facts.