Lossless compression preserves all original data during the compression process. It is commonly used for text and data where no loss of information is acceptable. In contrast, lossy compression sacrifices some data to achieve higher compression ratios, often used in multimedia applications where slight quality loss is acceptable.

Conceptual schema design concentrates on high-level business requirements, defining entities and relationships without delving into technical details. It's a blueprint for the overall structure, contrasting with logical schema design that involves table creation and relationship establishment.

Partition pruning contributes to query optimization by eliminating irrelevant partitions from the query execution process. This means that the database engine can skip unnecessary partitions, reducing the amount of data to be processed and improving query performance. This is especially crucial in large partitioned databases.

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.