Real-time Business Intelligence (BI) systems frequently use Stream Processing to capture and process incoming data streams quickly. Stream processing enables real-time analysis and decision-making by handling continuous data flows.

When a potential IT threat is identified, and its business impact is deemed to be very low, a suitable course of action is to monitor the situation and take no immediate action. It's essential to prioritize resources and efforts based on the level of risk and potential business impact. Replacing the software immediately or developing a comprehensive risk mitigation strategy may not be warranted for low-impact threats.

The OLAP operation that involves reducing the dimensionality of a data cube by fixing one or more dimensions at a single value is "Roll-up." This operation allows for aggregation or summarization of data, moving from lower-level detail to higher-level summaries, providing a more compact view of the cube.

Geospatial Visualization involves using geographic data to create maps that represent specific data points or trends. It allows the visualization of data in a geographical context, making it useful for location-based insights and decision-making.

The table in a data warehouse that typically contains metrics and key performance indicators (KPIs) is the Fact Table. Fact tables store numerical data related to business processes and events and serve as the core for data analysis and reporting in data warehousing.

Risk assessment is a fundamental component of IT risk management. It involves the identification and analysis of potential events or risks that could have adverse effects on the organization. By understanding these risks, organizations can develop strategies to mitigate or respond to them effectively.

In a typical Business Intelligence (BI) system, components such as data warehouses, data visualization tools, dashboards, and scorecards are integral. However, social media marketing is not a standard component of BI systems.

Denormalization is a database design technique where data is deliberately duplicated or grouped together to improve query performance. While it may lead to some data redundancy, it can significantly enhance data retrieval speed, making it useful in data warehousing scenarios.

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.