The most suitable approach in predictive analytics for predicting the likelihood of a loan applicant defaulting on a loan is classification. Classification models are designed to assign categories or labels to data, which in this case would be to categorize loan applicants as either likely to default or not based on historical data. This is a common use of predictive analytics in risk assessment.

In the context of data warehousing evolution, the shift from batch processing to real-time processing was a significant step towards Real-time Business Intelligence. Real-time processing allows organizations to access and analyze data as it's generated, enabling quicker decision-making and more agile operations.

The component primarily responsible for optimizing and executing complex queries efficiently in a Data Warehouse Appliance is the Query Optimizer. It analyzes queries and data distribution to generate efficient query execution plans, improving query performance.

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.