Data purging is the process of removing old or obsolete data from the data warehouse to free up storage space. This is essential for maintaining the efficiency and performance of the data warehouse by preventing it from becoming cluttered with outdated information.

For capturing daily price changes in a retail database, the most appropriate choice would be SCD Type 2, which allows for the historical tracking of changes to product prices. This type maintains a history of changes, making it ideal for scenarios where you need to preserve historical data. SCD Type 1 overwrites existing data, SCD Type 3 adds new records for changes, and SCD Type 4 is more complex and less commonly used.

Traditional Relational Database Management Systems (RDBMS) are commonly used for Online Transaction Processing (OLTP) applications. OLTP systems are optimized for short, quick transactions, such as order processing or record updates, where data integrity and consistency are paramount.

Data normalization is a data transformation technique that involves changing the scale of a variable so that it ranges between 0 and 1. This process is useful for comparing variables with different scales and ensuring that they contribute equally to data analysis and modeling.

Cloud-based Data Warehousing Solutions offer organizations increased scalability and flexibility. They allow organizations to scale their data warehousing resources as needed, eliminating the need for heavy on-premises hardware investments. This flexibility is crucial in handling dynamic data requirements and managing costs effectively.

Columnar databases are optimized for analytical workloads, and they store data in columns rather than rows. This allows for better compression because columns often contain more repetitive or identical values, which can be compressed more efficiently.

A Metadata Repository is typically used to catalog and manage an organization's metadata. Metadata includes information about data sources, data definitions, and data lineage, making it essential for data warehousing and data management.

In a data warehouse, a high number of joins in queries can indicate inefficiencies in query processing. Joins, especially complex ones, can impact performance. Performance tuning may involve optimizing or simplifying these joins to enhance query efficiency.

Data standardization is a common data transformation technique that helps reduce the influence of outliers in the dataset. It scales the data to have a mean of 0 and a standard deviation of 1, making it suitable for algorithms sensitive to the scale of the input features.

One of the primary benefits of Distributed Data Warehousing is improved query performance. By distributing data across multiple servers and nodes, queries can be processed in parallel, resulting in faster response times and better performance for analytical tasks.

Data summarization involves pre-aggregating or summarizing data, usually at a higher level of granularity, to improve query performance in the ETL process. This technique reduces the amount of data that needs to be processed during queries, resulting in faster and more efficient data retrieval.

Big Data solutions make extensive use of "Parallel" processing, which involves processing large datasets simultaneously across a distributed compute environment. This approach significantly enhances processing speed and efficiency when dealing with vast amounts of data.