Elasticity in cloud computing refers to the ability to dynamically scale resources up or down based on workload demands. It enables efficient capacity planning and scalability, allowing organizations to pay for only the resources they use. This is a key aspect of cloud computing efficiency.

The process of identifying and correcting (or removing) errors and inconsistencies in data is known as "Data Cleansing." Data cleansing involves detecting and resolving issues like missing values, duplicates, and inaccuracies, ensuring data quality and reliability.

The primary purpose of a Data Warehouse is to facilitate data analysis. Data Warehouses consolidate and store data from various sources, making it available for in-depth analysis, reporting, and decision-making. It provides a centralized repository for historical and current data, enabling businesses to gain insights and make data-driven decisions.

The "Data Access Layer" in a data warehouse architecture is responsible for providing a user-friendly interface that allows end-users to query the data without requiring them to write SQL queries. This component enhances accessibility and usability for non-technical users.

In a traditional Relational Database Management System (RDBMS), data is primarily stored in tables. These tables consist of rows and columns, where each row represents a record, and each column represents an attribute or field of the data. This tabular structure is designed for structured data storage.

Log transformation is often used in data transformation techniques to handle datasets with skewed distributions and outliers. It helps in making the data more symmetric and conforming to assumptions of statistical models. Additionally, it can reveal patterns that may not be evident in the original data.

The ETL phase responsible for pushing data into a data warehouse is the "Loading" phase. During this phase, transformed data is loaded into the data warehouse for storage and analysis.

Partitioning large tables in a data warehouse can significantly improve query performance. By dividing large tables into smaller, more manageable partitions, the system can access and process only the relevant data, leading to faster query responses. This strategy is particularly useful when dealing with large volumes of historical data.

To ensure fast analytical processing of sales data across multiple stores, prioritizing parallel processing capabilities is essential. Parallel processing allows the appliance to split and process queries across multiple processors or nodes simultaneously, significantly improving query performance for large datasets.

To ensure smooth video streaming for global users, a Content Delivery Network (CDN) is a suitable scalability solution. CDNs distribute content to multiple servers across the globe, reducing latency and improving the user experience by delivering content from a server that is physically closer to the user.