A "materialized view" in a data warehouse is a precomputed summary table that helps speed up query performance. It stores aggregated or computed data, reducing the need to perform complex calculations during queries, thereby improving response times for analytical queries. Materialized views are valuable in data warehousing for optimizing query performance.

To integrate supply chain management with financial accounting, the primary ERP module to consider is the Finance module. This module handles financial transactions, budgeting, and accounting processes, making it essential for tracking the financial aspects of the supply chain. While other modules are important, finance is the core module for financial integration.

The concept in ERP systems where data is entered once and can be accessed from multiple applications without redundancy is known as "Single Point Entry." This approach reduces data duplication and ensures data consistency and accuracy across the organization.

The primary challenge with incremental loads in ETL processes is detecting and synchronizing changes in the source data. Ensuring that only the changes are loaded while maintaining data integrity and consistency is a complex task. It often involves mechanisms to identify inserts, updates, and deletions in the source data and apply corresponding changes in the target system.

To track changes in customer addresses without retaining the history of previous addresses, you should implement SCD Type 1. This type overwrites existing data with the new address information, ensuring that only the most recent data is retained. SCD Type 2, 3, and 4 involve preserving historical data, which is not necessary in this scenario.

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.

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.

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.

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.

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.