Star Schema is more suitable for environments with high query complexity. Due to its denormalized structure and fewer join operations, it can handle complex queries more efficiently compared to the normalized structure of a Snowflake Schema, which involves more joins and tables.

Single-table inheritance involves embedding all attributes of the superclass and subclasses into a single table. This approach simplifies queries but may lead to null values and decreased performance for large datasets.

The primary function of a fact table in a data warehouse is to store transactional data, which typically consists of numerical values called facts. Fact tables contain foreign keys that link to dimension tables, allowing analysts to perform analysis and generate insights based on various dimensions.

A foreign key in an ERD serves the purpose of ensuring referential integrity between tables. It establishes a connection between two tables by referencing the primary key of one table as a foreign key in another, maintaining consistency and relational structure.

In NoSQL databases, a column-family store is a type of database that organizes data into tables with rows and columns but is optimized for handling wide-column stores. It is particularly suitable for scenarios where there are a large number of columns with varying data types.

In the Third Normal Form (3NF), all non-key attributes are fully functionally dependent on the primary key, and no transitive dependencies exist. This form aims to eliminate redundancy and dependency issues in a relational database.

Denormalization in this scenario involves introducing redundancy by merging the tables, reducing the need for JOIN operations. This can improve query performance for generating sales reports by minimizing the complexity of queries.

In database management, "storage optimization" refers to minimizing redundancy and improving efficiency in how data is stored. This involves techniques such as normalization, compression, and indexing to ensure that data is stored in the most efficient manner possible, reducing storage space and improving performance.

Data Warehouses are designed for analytical querying and analysis. They are optimized for complex queries that involve aggregations, summaries, and historical comparisons, providing valuable insights into trends and patterns within the data.

The process of carefully selecting which parts of a database schema to denormalize is called Strategic Denormalization. It involves a thoughtful approach to identifying specific areas where denormalization will bring the most significant performance benefits without sacrificing data integrity.