ACID stands for Atomicity, Consistency, Isolation, and Durability. These are the four key properties that guarantee the reliability of transactions in a database system.

Common methods used to monitor database performance include utilizing database performance counters to track key metrics, implementing query profiling to analyze query execution patterns, and employing specialized database monitoring tools to monitor system health and performance in real-time. These methods help identify performance bottlenecks and optimize database operations.

Database design tools like MySQL Workbench or Microsoft Visio use Entity-Relationship Diagrams (ERD) to help users visualize the database schema. ERDs illustrate the relationships between entities and their attributes, aiding in the understanding and planning of the database structure.

In relational schema design, a primary key uniquely identifies a record within a table. It serves as a unique identifier for each row and ensures data integrity in the table.

Boyce-Codd Normal Form (BCNF) is the highest level of normalization that a relational database can achieve. It ensures that there are no non-trivial functional dependencies of attributes on the primary key.

An attribute is said to be Transitive if it is dependent on the primary key, as well as on some other attribute(s) within the same table. This situation is resolved by normalization to reduce redundancy.

In this scenario, to enhance query performance, denormalization can be employed. Denormalization involves introducing redundancy in the database design to reduce the number of joins, leading to faster query execution. It's especially useful when dealing with read-heavy workloads and large datasets.

Conceptual schema design is closely related to Entity-Relationship modeling, where the focus is on defining entities, their attributes, and the relationships between them. This step ensures that the database accurately represents the real-world domain.

The process of denormalizing tables in Dimensional Modeling is known as Star Schema. In Star Schema, the Fact table is surrounded by Dimension tables, creating a star-like structure. This denormalized design enhances query performance for analytical queries and reporting.

Data Partitioning is a technique used to improve storage efficiency by dynamically allocating storage space based on data access patterns. It involves dividing large datasets into smaller, more manageable partitions. This helps in optimizing query performance and storage utilization.