A Unique key constraint in a relational database ensures that values in a column or a set of columns are unique across the entire table. This constraint is crucial for maintaining the uniqueness of data in a specific column or combination of columns.

Data consistency differs between Key-Value Stores and traditional relational databases primarily because Key-Value Stores often prioritize availability and partition tolerance over strong consistency. This means that in distributed environments, Key-Value Stores may exhibit eventual consistency rather than immediate consistency.

Data partitioning contributes to database performance by enhancing query performance. It involves dividing large tables into smaller, more manageable pieces, allowing the database system to access and process only the relevant partitions, resulting in faster query execution.

Denormalization in relational schema design involves intentionally introducing redundancy for performance optimization. This technique is employed to simplify queries and enhance read performance, especially in scenarios where read operations significantly outnumber write operations.

Denormalization is commonly applied in OLAP (Online Analytical Processing) systems where read performance is critical. OLAP systems are optimized for complex queries and reporting, making denormalization beneficial for improved query performance.

Maintaining data integrity in a healthcare system involves regularly auditing and monitoring data changes. This helps identify any unauthorized or accidental modifications to patient records, ensuring the accuracy and reliability of medical information. Hashing adds security but is not directly related to data integrity. Direct user modifications and version control may introduce risks to data integrity.

Communication is essential in data modeling collaboration. It involves effective interaction and exchange of ideas among team members, stakeholders, and subject matter experts. Clear communication ensures that everyone is on the same page, leading to accurate and comprehensive data models.

In data warehousing, the term "grain" refers to the level of detail or granularity at which data is stored in the fact table. It defines what each row in the fact table represents, whether it's at a daily, monthly, or another level of detail. Determining the appropriate grain is crucial for accurate analysis and reporting.

The appropriate relationship for this scenario is Many-to-Many. Each user can have multiple posts, and each post can have multiple comments from different users. It reflects the complex interactions between users, posts, and comments on a social media platform.

Sharding is a database design technique where large tables are broken down into smaller, more manageable parts called shards. Each shard is an independent database table, which can enhance performance by distributing the data across multiple servers or nodes. This technique is often used in distributed databases.