"Data Profiling" is a critical ETL concept that assists in understanding and analyzing the data quality, structure, and content. It helps in identifying discrepancies, anomalies, and inconsistencies in the data, which would be useful in tracing back the origin of data discrepancies in the sales data.

To ensure data consistency and integrity after adding new data sources, it is crucial to prioritize the implementation of data quality checks. These checks can identify discrepancies, anomalies, and errors in the incoming data, allowing you to address data quality issues early and maintain the reliability of your aggregated reports.

Disaster recovery testing is the practice of regularly testing the data warehouse recovery process to verify that it can be successfully restored in case of a failure or disaster. This testing ensures that the backup and recovery procedures are reliable and that the organization can quickly recover its data and resume operations if needed.

In-Memory Processing is a feature of Data Warehouse Appliances that speeds up query performance by reducing I/O operations. This technique involves storing data in memory for faster access, bypassing the need to read data from disk, which is a time-consuming process. It significantly improves query response times.

ETL (Extract, Transform, Load) tools frequently offer a "Graphical" interface that enables users to design data flow and transformations visually, without the need to write code. This graphical interface simplifies the development of ETL processes and makes it more accessible to a wider range of users.

The characteristic of data warehousing that enables the storage of data over extended time periods, allowing for the analysis of historical trends and changes, is often referred to as "Data Temporality." This feature is crucial for historical data analysis and trend identification in data warehousing.

One of the significant challenges in the evolution of data warehousing with the advent of Big Data is the management of unstructured and semi-structured data. Traditional data warehousing systems are designed for structured data, but Big Data often includes diverse data types, such as text, images, and social media posts, which require specialized handling.

The architecture that involves collecting data from various sources and consolidating it into a central repository is known as Data Warehousing. This centralization facilitates efficient data management, reporting, and analysis.

The concept of "hierarchy" in data modeling organizes data into structured levels, making it easier to drill down into detailed data or roll up to higher-level summaries for analytical purposes. This structured organization enables efficient exploration and analysis, helping analysts navigate and understand complex datasets.

Data indexing is the process of creating data structures that optimize query performance without altering the original data. This technique involves creating index structures that store a subset of data in a way that speeds up data retrieval for specific queries.