Cloud-based data warehousing solutions are often "Horizontally" scalable, allowing them to adjust to workload demands in real-time by adding or removing resources horizontally, such as adding more servers or clusters. This scalability is a key advantage of cloud-based data warehousing, ensuring performance and flexibility.

In this scenario, a Data Mart would be the most suitable data storage solution. A Data Mart is a specialized data repository designed for a specific business function or department, making it ideal for isolating and analyzing sales data for the clothing department while excluding other unrelated data. It provides a more focused and efficient way to store and access department-specific information.

Columnar storage databases optimize query performance in big data scenarios by encoding and compressing data in a columnar format. This minimizes the amount of data read from storage, leading to faster query execution. It also enhances compression, reducing storage requirements.

A dashboard is a crucial tool in data warehouse monitoring. It offers a visual representation of the system's performance metrics in real-time. Dashboards help data professionals track key performance indicators and quickly identify issues or opportunities for optimization.

In this scenario, cloud bursting is the appropriate capacity planning strategy. It allows the company to use cloud resources to handle sudden traffic surges. When on-premises resources are insufficient, the organization can "burst" into the cloud temporarily to meet the increased demand, ensuring a seamless user experience.

Data imputation is a data cleaning technique that involves using algorithms to guess or estimate missing values in a dataset based on the values of other data points. It's essential for handling missing data and ensuring that datasets are complete and ready for analysis.

Snowflake is a cloud-based data warehousing solution that uses a multi-cluster shared architecture. This architecture allows for concurrent read and write access, making it suitable for large-scale, high-performance data warehousing and analytics tasks.

Data masking is primarily implemented in data warehousing to safeguard sensitive data from unauthorized access. It involves replacing or concealing sensitive information with fictional or masked data while maintaining the data's format and usability for authorized users. This is crucial for compliance with data privacy regulations and protecting confidential information.

"Data skewness" in data profiling refers to the tendency of data to be unbalanced or non-uniformly distributed. It indicates that the data has a skew or imbalance in its distribution, which can affect statistical analysis and modeling. Understanding skewness is crucial in data analysis and decision-making.

In Slowly Changing Dimension (SCD) Type 2, changes in dimension attributes are handled by marking the old record as inactive and inserting a new record with the updated data. This allows historical tracking of attribute changes.