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.

The typical scope of a Data Warehouse involves storing historical and aggregated data. Unlike operational databases that handle day-to-day transactions, a Data Warehouse focuses on providing a consolidated view of historical data for analytical purposes. This enables better decision-making and trend analysis.

Tools like ER Diagram Tools and Database Design Tools play a crucial role in visualizing and designing database structures during both Forward and Reverse Engineering. They assist in creating visual representations, ensuring efficient communication between stakeholders and aiding in the overall design process.

A graph database handles complex relationships by representing relationships as first-class citizens. This means that relationships are treated as important and distinct entities, allowing for efficient traversal and querying of complex networks of connected data. This approach simplifies the representation and querying of intricate data relationships.

Concurrency allows data modelers to work on the same project simultaneously, enabling parallel development without conflicts. This collaborative approach enhances productivity and accelerates the data modeling process.

In data partitioning, the term "sharding" refers to distributing data across multiple servers based on a sharding key. Sharding helps in horizontal scaling by distributing data subsets, or shards, across multiple servers, thereby improving scalability and performance of the database system.

Clustering can help optimize the database by grouping similar transactions together. By clustering transactions based on similarities such as transaction type, amount, or customer, it reduces the number of I/O operations required to access relevant data, thus improving query processing speed.

The multiplicity of a relationship indicates the maximum number of occurrences of one entity that can be associated with each occurrence of another entity. It defines the range of associations between entities in terms of minimum and maximum occurrences.

The primary difference is that document-based NoSQL databases store data as documents with a flexible schema, allowing for nested structures and varied data types. Key-value stores, on the other hand, store data as simple key-value pairs, providing a more straightforward structure with no nested elements.