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.

A staging area in ETL processes is used to temporarily store data before it's transformed and loaded into the data warehouse. It allows for data validation, cleansing, and transformation without impacting the main data warehouse, ensuring data quality before final loading.

Amazon Redshift is a fully managed, performance-tuned data warehousing service provided by AWS. It is designed for analyzing large datasets and offers features like automatic backup, scaling, and optimization to ensure efficient data warehousing in the cloud.

Real-Time Business Intelligence (BI) systems are designed for real-time data processing and analysis. They provide insights and decision-making capabilities in the moment, making them ideal for scenarios where immediate responses to user actions or events are required, such as making real-time offers based on browsing behavior.