In a Data Warehouse, data is often stored in a Star schema. The Star schema consists of one or more fact tables referencing any number of dimension tables, forming a star-like structure. This schema design facilitates efficient querying and reporting for analytical purposes.

Partitioning in database systems contributes to performance enhancement by distributing data across multiple disks or servers. This parallelization can lead to improved query response times and more efficient data retrieval.

Inheritance in database modeling allows for Specialization and Extension of attributes and relationships. Specialization involves creating subtypes with additional attributes, while extension involves adding new relationships to existing entities.

Inheritance allows entities of different types to be treated uniformly based on their shared superclass. This concept promotes code reusability and a more intuitive representation of relationships between entities.

In an ERD context, an attribute refers to a property or characteristic of an entity. Attributes describe the details or features of an entity, helping to define the information that can be stored about each instance of that entity.

A relational schema in database design represents the logical structure of the database. It defines the tables, attributes, and relationships between tables without detailing how the data is stored physically. It's crucial for organizing and understanding the database's structure.

In data modeling, aggregation involves the grouping of detailed data into a higher-level view, often using functions like COUNT, AVG, etc. Summarization, on the other hand, is the process of creating a concise summary of data, providing a more comprehensive overview. Understanding this difference is crucial for effective data modeling and reporting.

A cardinality of "One-to-One" in an ERD signifies that each entity in the first table can be related to only one entity in the second table, and vice versa. This type of relationship is less common but is useful in certain scenarios where a strict one-to-one association is needed.

Entropy compression algorithms utilize statistical methods to represent data more efficiently. These algorithms analyze the frequency of symbols in the data and assign shorter codes to more frequent symbols, resulting in overall compression.

Key-Value Stores are typically associated with horizontal scalability, where the system can handle increased load by adding more machines or nodes. This enables better distribution of data and load across multiple servers, ensuring efficient and scalable performance.