Relationships in an ERD are represented by a diamond shape. This symbolizes the connection between entities, indicating how data is associated and shared between them.

Advanced ER diagram tools often provide validation functionality to ensure data consistency. This involves checking the diagram against predefined rules and constraints, helping users identify errors or inconsistencies in their data model. Validation is crucial for maintaining the integrity of the database design.

Using a column-family store over a relational database provides advantages like improved scalability for read-heavy workloads. Column-family stores are designed to efficiently handle large amounts of data with high read-throughput, making them suitable for applications with demanding query patterns and analytical workloads.

In data warehousing, Dimensional Modeling involves aggregating data at a higher level of granularity. It focuses on organizing data in a way that supports efficient querying and reporting, often using fact and dimension tables to represent business processes.

Techniques like vector clocks are commonly used to achieve eventual consistency in NoSQL databases. Vector clocks help in tracking and ordering updates across distributed nodes, enabling eventual convergence of data while allowing for some level of asynchrony.

Modality in a relationship between entities indicates the presence or absence of entities in the relationship. It describes whether a related entity is optional or mandatory, providing insights into the completeness of the relationship.

Collaboration features in ER diagram tools for intermediate users are significant as they facilitate teamwork and concurrent design. Multiple team members can work on the same project simultaneously, ensuring efficient collaboration and reducing the chances of conflicts in the database design process.

Partitioning in database management serves the purpose of organizing large datasets into smaller, more manageable segments. It helps in improving query performance, data availability, and maintenance tasks by dividing the data into logical partitions based on predefined criteria such as range or hash values.

The main purpose of a fact table in Dimensional Modeling is to store quantitative data, typically numerical values that represent business facts. These facts are often measures such as sales, revenue, or quantities. Fact tables are linked to dimension tables, providing context and additional details for the stored facts, facilitating meaningful analysis.

UML and SQL serve different purposes in data modeling. UML is a visual modeling language primarily used for modeling software systems, focusing on object-oriented concepts such as classes, objects, and relationships. SQL, on the other hand, is a database query language used for managing and manipulating relational databases. While UML can represent the structure and behavior of a system, SQL is used for defining and querying the data stored in databases.