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.

Compression techniques are best suited for images and multimedia data. These types of data often contain redundant information that can be efficiently compressed without significant loss of quality. Structured and unstructured data may not benefit as much from compression, depending on the nature of the data.

In document-based modeling, relationships between documents are typically represented through embedded documents. This means that one document can contain another document within it, forming a hierarchical structure. This approach simplifies data retrieval and management in document databases.

A Relational Database would be most appropriate for this use case. Relational databases excel at handling structured data and are well-suited for scenarios where data consistency and complex queries are crucial, such as storing product information in an e-commerce website.

Database normalization contributes to data integrity by reducing redundancy and dependency among data. By organizing data into tables and eliminating data duplication, normalization minimizes the chances of inconsistencies and update anomalies. It ensures that data is stored logically and efficiently, promoting accuracy and reliability.

For a retail company analyzing sales data, a Star Schema would be more appropriate. Star Schema denormalizes data, simplifying queries and enhancing performance, crucial for analytical tasks common in sales analysis. Its structure with a central fact table surrounded by dimension tables suits the needs of reporting and analysis in retail sales, where querying across different dimensions like time, product, and geography is essential.

JSON (JavaScript Object Notation) is commonly used for documents in document-based modeling. JSON provides a lightweight, human-readable format that is easy to parse and manipulate. It is well-suited for representing semi-structured data commonly found in document databases.

The primary goal of denormalization is to optimize database performance. By reducing the number of joins and simplifying data retrieval, denormalization enhances query performance, making it suitable for scenarios where read operations are frequent.

In an ERD, a Derived attribute is one that can be derived or calculated from other attributes in the database. It doesn't store data physically but can be computed based on other attribute values.