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.

To alleviate performance bottlenecks in the ETL process during the transformation phase, the company should consider implementing parallel processing. Parallel processing allows multiple transformations to occur simultaneously, which can significantly improve ETL performance by utilizing available system resources more efficiently. It reduces the time taken to complete the transformation phase.

Capacity planning in data warehousing involves predicting the future data warehouse load or traffic based on historical data and trends. This process helps ensure that the data warehouse infrastructure can handle increasing demands and maintain optimal performance.

To understand the factors causing the spike in sales during specific months, the organization should delve into Data Analytics. Data Analytics involves using statistical and analytical techniques to extract insights and draw conclusions from data, helping to uncover the underlying reasons behind trends.

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.