Type 2 dimension change captures historical data without overwriting existing records. It adds new records for each change, including a surrogate key and an effective date range, allowing for historical analysis while preserving the original data.

Data Clustering involves physically grouping related data together in storage. This process helps improve query performance by reducing the need for extensive disk I/O operations when retrieving related data. It enhances the efficiency of data retrieval operations, especially in scenarios where related data is commonly accessed together.

Aggregation techniques in Dimensional Modeling involve pre-calculating and storing aggregated data to enhance query performance. This reduces the need for complex computations during queries, making analytical processing faster and more efficient in data warehouses.

Choosing the appropriate SCD technique involves considering factors such as the volume of data changes, query performance, and data storage requirements. Each type of SCD has its strengths and weaknesses, and the decision should align with the specific needs and characteristics of the data warehouse environment.

A key characteristic of document databases is their dynamic schema. Unlike traditional relational databases with fixed schemas, document databases allow for flexible schema designs where each document can have its own unique structure. This flexibility accommodates evolving data requirements and simplifies the development process.

In this scenario, a Many-to-Many relationship is appropriate. This allows each employee to work on multiple projects, and each project to have multiple employees associated with it. It involves a junction table to manage the relationship.

In an ERD, a composite key is a key that consists of more than one attribute. It is used to uniquely identify an entity when a single attribute is not sufficient. Composite keys are common in situations where a combination of attributes is needed for uniqueness.

In denormalization, the creation of Aggregate tables is a technique to store precomputed results. These tables contain summarized data, reducing the need for complex calculations during query execution and improving overall performance.

Slowly Changing Dimensions (SCD) in data modeling are used to capture changes to dimension data over time. They allow for the tracking of historical data and help maintain a history of changes to dimensional attributes, which is crucial for analysis and reporting purposes.

In database design, "Forward Engineering" involves generating a database schema from a higher-level model, such as a conceptual data model. This process moves from an abstract representation to a concrete implementation, helping in the actual creation of the database structure.