The main advantage of distributing data across multiple storage devices or locations in a Distributed Data Warehousing setup is scalability and load balancing. It allows for the efficient distribution of data, ensuring that query workloads can be evenly spread across resources, thus optimizing performance and handling increased data volumes effectively.

In cloud environments, data redundancy and high availability are frequently accomplished through "Data Replication," which involves duplicating data across multiple zones or regions. This redundancy ensures that data remains accessible and intact, even in the event of hardware failures or other disruptions.

A histogram is the most suitable chart for displaying the distribution of a single continuous dataset. It shows the frequency of data points in specific intervals, providing insights into the data's distribution and central tendencies. It's commonly used in statistics and data analysis.

SCD Type 2 is the type of Slowly Changing Dimension that uses a separate table to store both the current and historical data for an attribute. It allows you to maintain a historical record of changes over time while preserving the current value in the main table. This is particularly useful in data warehousing for tracking changes to dimension attributes.

In a star schema, dimension tables are typically denormalized, meaning that hierarchies of attributes are not broken into separate tables. This design is contrary to the snowflake schema, where attributes are often normalized into separate tables to reduce redundancy. In a snowflake schema, the Year, Quarter, and Month attributes might be split into separate tables, leading to more complex joins.

The "in-memory" aspect of a data warehouse means that data is stored in random-access memory (RAM) for faster access and processing. Storing data in RAM allows for high-speed data retrieval and analytics, as data can be accessed more quickly compared to traditional storage on external devices like hard drives. This leads to improved query performance and faster data analysis.

The strategy that involves splitting the data warehouse load process into smaller chunks to ensure availability during business hours is known as "Data Partitioning." Data is divided into partitions, making it more manageable and allowing specific segments to be loaded or accessed without disrupting the entire system. This is a common strategy for balancing data warehouse loads.

Using a snowflake schema, which involves normalizing dimension tables, can lead to increased data redundancy. Normalization breaks down attributes into separate tables, which can result in more complex join operations, increased storage requirements, and potentially slower query performance due to the need for multiple joins.

The "Data Access Layer" in a data warehouse architecture is responsible for providing a user-friendly interface that allows end-users to query the data without requiring them to write SQL queries. This component enhances accessibility and usability for non-technical users.

In a traditional Relational Database Management System (RDBMS), data is primarily stored in tables. These tables consist of rows and columns, where each row represents a record, and each column represents an attribute or field of the data. This tabular structure is designed for structured data storage.