In data warehousing, the process of Extracting, Transforming, and Loading (ETL) data is crucial. ETL involves extracting data from source systems, transforming it to fit the data warehouse schema, and loading it into the data warehouse for analysis. It ensures data quality and consistency.

Data cleansing operations, like removing duplicates and correcting data inconsistencies, are typically performed during the Transformation phase of the ETL process. This is when data is prepared for storage in the data warehouse and is where data quality improvements are made.

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.

The process that pre-aggregates data to enhance query performance in a data warehouse is ETL (Extract, Transform, Load). During ETL, data is transformed and summarized, allowing queries to access pre-computed aggregations, which significantly improves query response times.