Graph databases are particularly suitable for recommendation systems and social networks due to their native representation of relationships. In a graph database, relationships are first-class citizens, making it efficient to traverse and analyze connections between entities, a key requirement in recommendation systems and social networks.

Type 4 SCD handles historical data by using effective dating or versioning. Instead of adding new rows, it maintains the original row with an effective date or version number, allowing for a more compact representation of historical changes without cluttering the table with additional rows.

Considering the large volume of multimedia content, a social media platform should consider Lossy Compression. This method sacrifices some data to achieve higher compression ratios, making it suitable for non-critical content like images and videos where a slight loss in quality is acceptable for significant storage savings.

CAP theorem states that in a distributed system, it's impossible to achieve all three of Consistency, Availability, and Partition Tolerance simultaneously. Understanding CAP helps in making informed decisions when designing and selecting NoSQL databases for specific use cases.

In a Star Schema, a Conformed dimension table contains reference attributes that are consistent and shared across multiple fact tables. This allows for uniformity in reporting and analysis, as the same dimension can be used across different parts of the data warehouse.

Common challenges in conceptual schema design include dealing with ambiguous requirements, where clarity is lacking. Clearing up ambiguities is crucial to ensure the final schema accurately reflects business needs.

The NOT NULL constraint in a column definition specifies that the column must not contain NULL values. It enforces the presence of data in that column, ensuring that each record has a valid and meaningful value for that particular attribute.

A multivalued attribute in database modeling is an attribute that can have multiple values for each entity. This concept is used to represent situations where an entity can have multiple values for a specific attribute, without creating a separate entity for each value.

Database tuning involves adjusting the physical design and configuration parameters of a database to optimize its performance. This includes optimizing queries, indexing, and other settings to ensure efficient data retrieval and processing. Tuning is an ongoing process to adapt the database to changing requirements and workload.

A Data Mart typically focuses on a specific functional area. Unlike a Data Warehouse, which covers the entire organization, a Data Mart is designed for a particular business unit or department's specific needs, providing more targeted insights.