In database modeling, a primary key is a key that uniquely identifies a record within a table. It serves as the unique identifier for each row and ensures data integrity by preventing duplicate or null values in this key field.

Key considerations when designing a conceptual schema include maintaining data integrity, ensuring simplicity, and applying normalization techniques. These factors contribute to a robust and efficient database design.

Functional dependency and multi-valued dependency are distinct concepts. Functional dependency deals with one-to-one relationships within a table, whereas multi-valued dependency handles situations where one attribute uniquely determines another, allowing for multiple values for the same determinant.

One key feature of document-based databases is eventual consistency. This consistency model prioritizes availability and partition tolerance in distributed systems, ensuring that all nodes eventually reach a consistent state despite potential network delays or failures. This makes document-based databases efficient for distributed data management in scenarios where real-time consistency is not a strict requirement.

Partitioning based on Attributes involves dividing data based on specific ranges of values. This technique is commonly used to organize and manage large datasets efficiently, improving query performance and data retrieval.

Sharding is the process of distributing data across multiple servers in a NoSQL database. It helps in improving performance and scalability by dividing the dataset into smaller, manageable parts that can be processed independently.

Data integrity in a database system ensures the consistency of data, meaning that the data is accurate, valid, and reliable throughout its lifecycle. It prevents inconsistencies and errors in the database.

The process of organizing data into multiple related tables while eliminating data redundancy is known as normalization. Normalization is crucial for maintaining data integrity and reducing data anomalies in a relational database.

A key difference is that Forward Engineering involves generating a database schema from a conceptual model, moving from high-level design to implementation. In contrast, Reverse Engineering does the opposite, analyzing existing code or structures to create a conceptual model.

In this scenario, representing full-time and part-time workers as separate entities using Generalization and Specialization is the appropriate approach. Each entity can have its own set of attributes and behaviors, allowing for clear modeling and differentiation between the two types of employees.

Clear communication is crucial for effective collaboration in data modeling. It ensures that team members understand each other's perspectives, requirements, and decisions, promoting a cohesive and efficient modeling process.

Various strategies can be employed for handling changing dimensions, including overwriting existing data, adding new records with new keys, and creating separate tables for historical data. The choice depends on the specific requirements of the business and the nature of the dimension changes.