Dimensional modeling is a technique used in data warehousing that enables the analysis of data at different granularities, such as year, quarter, month, and day. It involves creating fact tables and dimension tables to represent data hierarchies, facilitating efficient querying and reporting.

For an organization needing immediate insights into real-time business operations, Operational Business Intelligence (Operational BI) is most suitable. Operational BI focuses on monitoring and analyzing current data to support daily decision-making and operational activities. It provides up-to-the-minute information for faster response.

Traditional RDBMS (Relational Database Management Systems) are typically better suited for OLTP (Online Transaction Processing) operations, which require fast and efficient handling of numerous small, concurrent transactions.

To handle a high volume of transactions with quick read and write capabilities and consistency, a NewSQL database is the most appropriate choice. NewSQL databases combine the benefits of SQL (consistency) and NoSQL (scalability and speed) databases.

The primary advantage of columnar databases for analytics and querying is their ability to quickly aggregate and query large datasets. Columnar databases store data in a column-wise fashion, which is more suitable for analytical workloads as it allows for faster data retrieval and processing for tasks like aggregation and reporting.

A "Data Mart" is a subset of a data warehouse that focuses on a specific subject or department, such as sales, finance, or a particular area of an organization. It contains data relevant to a particular business unit or group, making it easier to access and analyze data related to specific needs.

To achieve a unified view of data from various sources, the pharmaceutical company should consider implementing a Data Warehousing solution. Data Warehousing involves the process of centralizing, storing, and organizing data from disparate sources into a structured repository, making it accessible for analysis and reporting.

A database administrator may choose to denormalize a database to optimize data storage and retrieval performance. Denormalization involves reducing the number of tables and increasing redundancy, which can speed up query performance, particularly in data warehousing where complex queries are common. However, it may come at the cost of some data integrity and consistency.

The primary challenge of "data silos" in the context of ERP (Enterprise Resource Planning) is ensuring that data is accessible and integrated across various departments and modules within the organization. Data silos result in isolated information that can hinder effective decision-making and collaboration. Integrating data from different sources is essential for ERP to deliver its full benefits.

In this situation, you should rename the columns to have the same name. It ensures consistency and clarity in the dataset, making it easier to work with. This step is crucial for data integration and analysis as it avoids redundancy and confusion that might arise from having multiple column names for the same data.

The primary goal of Business Intelligence (BI) is to provide data insights and support decision-making. BI systems gather, process, and analyze data to help organizations gain a deeper understanding of their business and make informed choices based on data-driven insights.

A Data Warehouse provides a consolidated and consistent view of data sourced from various systems across an organization. It is designed to support data analysis and reporting by providing a centralized repository for structured data from different sources.