The primary goal of data mining is pattern discovery within large datasets. Data mining techniques aim to uncover hidden patterns, relationships, and trends in data that are not immediately apparent, allowing for informed decision-making and insights.

A data mart is typically smaller in scope compared to a data warehouse. It focuses on a specific business area, such as sales or marketing, and contains a subset of data from the data warehouse tailored for the needs of a particular department or group. Data marts are designed to provide specialized insights and support decision-making within a specific context.

In an ERP system, the module that typically manages human resources, payroll, and benefits is the Human Resources module. This module handles personnel information, payroll processing, and employee benefits, making it essential for managing HR functions in an organization.

In data profiling, descriptive statistics provide a summary of the central tendency of the data. This includes measures like mean, median, and mode, which help understand the typical values in the dataset and how they are distributed. Descriptive statistics are essential for data analysis and decision-making.

Cloud deployment in ERP involves accessing software applications over the internet, often provided by third-party vendors. This method offers flexibility, scalability, and reduced infrastructure costs compared to on-premises deployments. It has gained popularity for its accessibility and cost-effectiveness.

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 "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.

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.

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.

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.