The process of cleaning, transforming, and enhancing the data to prepare it for loading into a data warehouse is called "Data Transformation." During this phase, data is cleansed, structured, and enriched to ensure its quality and consistency for analysis.

Data replication is a strategy in data warehousing that involves creating copies of the data warehouse at regular intervals. This approach helps minimize data loss in case of failures by ensuring that there are up-to-date backup copies of the data readily available. Data replication is essential for data resilience and disaster recovery.

To address memory overloads in a data warehouse, implementing data partitioning is a strategic maintenance strategy. Data partitioning involves dividing large tables into smaller, more manageable segments. This can reduce the memory requirements and improve query performance during peak hours.

Data clustering is a technique used in data warehouses to determine the physical order of data within a table. It is done to group similar data together, optimizing query performance by reducing the need to access scattered data.

Explanation:

In data warehousing, ETL stands for "Extract, Transform, Load." This process involves extracting data from source systems, transforming it into a suitable format, and loading it into the data warehouse. Transformation includes data cleansing, validation, and structuring for analytical purposes.

Time series forecasting is a predictive analytics method that focuses on modeling and forecasting future values based on historical time-ordered data. It is commonly used in various fields, including finance, economics, and demand forecasting.

The methodology that emphasizes a phased approach to deploying ERP solutions, where each phase builds on the previous one, is called the "Incremental Approach." In this approach, each phase is a stepping stone toward achieving the final ERP solution, ensuring a structured and manageable implementation.

"Product Type," "Brand," and "Region" are considered dimensions in the data model. Dimensions are attributes used for analyzing and categorizing data, while measures (like "Total Sales" and "Number of Items Sold") represent the numeric values to be analyzed.

When dealing with a dataset containing significantly higher values that skew results, log transformation is a valuable technique. It compresses the range of values, making it easier to manage extreme values and stabilize variances. This is particularly useful in scenarios like financial data analysis or when dealing with data with a heavy right-skew.