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.

Columnar databases are frequently preferred in scenarios with heavy aggregation operations. This is because their column-oriented storage allows for efficient processing of aggregation functions, making them well-suited for analytical and data warehousing workloads where aggregations are common.

To ensure quick recovery without data inconsistency in case of an ETL process failure, the retail company should consider using checkpoints and logging. Checkpoints allow the process to save its progress at various stages, and logging records all activities and changes. In case of failure, the process can resume from the last successful checkpoint, minimizing data inconsistencies and potential data loss.

In the context of dashboards, a "Tile" is used to describe a graphical representation that provides at-a-glance views of key performance indicators (KPIs). Tiles are often customizable components that display summarized data or metrics, making it easy for users to monitor and understand essential information.

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.

Log transformation is often used in data transformation techniques to handle datasets with skewed distributions and outliers. It helps in making the data more symmetric and conforming to assumptions of statistical models. Additionally, it can reveal patterns that may not be evident in the original data.