In a star schema, the fact table contains the measures (quantitative data) and foreign keys that connect to dimension tables. Dimension tables hold descriptive information about the data, so the foreign keys in the fact table point to the dimension tables, allowing you to analyze the measures in context.

The Random Forest technique is used for classification and regression tasks. It constructs a multitude of decision trees during training and combines their results to improve accuracy and reduce overfitting. This ensemble approach is effective for predictive modeling.

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.

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.

Scaling out, also known as horizontal scaling, involves adding more machines or nodes to a system to handle increased load. It's a strategy used to improve a system's performance and capacity by distributing the workload across multiple resources.

In the ETL (Extract, Transform, Load) process, the first phase is "Extraction." This phase is responsible for gathering data from various sources, such as databases, flat files, and cloud sources, and extracting it for further processing and storage in a data warehouse.

Tableau is a widely recognized BI tool known for its capability to handle large datasets and create interactive dashboards. It offers a user-friendly interface for data visualization, making it a preferred choice for data professionals and analysts.