Denormalization is a schema design approach where data is intentionally duplicated or grouped together beyond the strict requirements of normalization. This is done to improve query performance and simplify data retrieval at the cost of redundancy.

Surrogate keys in Dimensional Modeling serve a crucial role by uniquely identifying each record, eliminating dependencies on natural keys, and simplifying data warehouse maintenance. They contribute to data warehouse performance and ease of management.

To ensure effective collaboration among teams in a data modeling project, using collaborative tools and platforms is essential. These tools allow teams to share documents, communicate in real-time, track changes, and coordinate tasks seamlessly across different locations, fostering collaboration and efficiency.

Employing materialized views is a strategy for implementing denormalization effectively. Materialized views store precomputed results, reducing the need for complex joins and enhancing query performance in a denormalized schema.

Effective start and end dates in SCD define the period during which a record is valid. These dates help in tracking changes over time, enabling historical analysis and ensuring accurate representation of data at any given point.

Decompression is the process of reversing compression, restoring the compressed data back to its original form. It is a crucial step in the compression-decompression cycle, ensuring that the data can be effectively used after compression.

The purpose of relationships in graph databases is to express connections or associations between entities. By defining relationships between nodes (entities), it becomes possible to navigate and query the database based on the connections, providing a more natural and intuitive representation of data relationships.

Database partitioning can enhance performance tuning efforts by reducing disk I/O. By dividing large tables into smaller partitions, the database system can access and manipulate data more efficiently, resulting in reduced disk read/write operations and faster query execution times. This helps in improving overall database performance and scalability.

Forward Engineering would be more suitable to start with in this scenario. It involves starting from the requirements and designing the database system accordingly. This ensures that the database structure aligns with the business needs, making it a logical starting point for development.

Slowly Changing Dimensions (SCD) is a Dimensional Modeling technique used to handle changes in dimension attributes over time. SCDs are categorized into different types (Type 1, Type 2, etc.) based on how they manage historical changes in data, ensuring accuracy in analytical reporting.

Tagging is the aspect of version control that facilitates creating multiple versions of the data model to explore different design options. With tagging, specific points in the project's history are marked, allowing the team to refer back to or recreate a particular version if needed.

Reverse Engineering starts with a Conceptual Data Model. It involves analyzing an existing database schema to understand the business rules and requirements that led to its creation. This helps in creating a high-level representation of the data structure.