Data consistency in data warehousing ensures that data remains consistent and unchanged during a user query, even if the underlying data changes. This is typically achieved through techniques like snapshot isolation or locking mechanisms to maintain data integrity for concurrent user queries.

Throughput is a common metric used in capacity planning to measure the maximum amount of work a system can handle. It quantifies the number of tasks, transactions, or data that can be processed within a specified time frame, helping organizations ensure their systems can meet performance requirements.

In OLAP, the "Roll-up" operation is used to provide a summarization of data, increasing the level of abstraction. It allows users to move from lower levels of detail to higher, more aggregated levels, simplifying complex data for higher-level analysis and reporting.

In this situation, it's essential for the company to reevaluate their core business processes and determine whether the ERP system can be adapted to align with these processes. Making necessary changes to the software or processes may be required to ensure the ERP system meets the company's needs. Simply continuing, starting from scratch, or ignoring the misalignment can lead to inefficiencies and project failure.

The retail company should employ the data mining technique of clustering. Clustering helps identify groups of items or customers with similar characteristics, making it suitable for discovering which products are often bought together. It can provide valuable insights for marketing and inventory management.

Surrogate keys are artificial keys used in fact tables to ensure consistency and improve join performance. They are typically system-generated and have no business meaning, making them suitable for data warehousing purposes. Surrogate keys simplify data integration and maintain data integrity.

In data transformation, "binning" involves sorting data into categories or intervals. It is used to reduce the complexity of continuous data by grouping it into bins. Binning can help in simplifying analysis, visualizations, and modeling, especially when dealing with large datasets.

For a multinational corporation looking to unify its data infrastructure and aggregate data from various departments, regions, and functions, a Data Warehouse is the appropriate choice. Data Warehouses are designed to consolidate and centralize data from across the organization, providing a unified platform for analysis and reporting. They ensure that data is consistent and easily accessible for decision-makers across the corporation.

The component responsible for extracting, transforming, and loading (ETL) data in a data warehousing environment is the ETL (Extract, Transform, Load) tool. ETL tools ensure that data from various sources is collected, cleansed, and loaded into the data warehouse efficiently and accurately.

Data imputation is a method used to handle missing data in a dataset. It involves estimating or filling in the missing values using various techniques, such as mean, median, or machine learning algorithms. This ensures that the dataset remains complete for analysis and modeling.