A data warehouse is a central repository that stores and manages large volumes of data from various sources, enabling efficient data analysis and decision-making.

ELT (Extract, Load, Transform) is not a common data integration technique. ETL involves extracting data from source systems, transforming it, and then loading it into the data warehouse. ELT, on the other hand, involves extracting data from source systems, loading it into the data warehouse, and then transforming it.

Data cleansing is the process of identifying and correcting errors and inconsistencies in data. It involves techniques such as data standardization, data deduplication, and data validation.

Normalization is not a common data transformation technique. Normalization is a database design technique that involves organizing data into tables and columns to reduce data redundancy and improve data integrity. Aggregation, sorting, and filtering are common data transformation techniques used to manipulate and summarize data.

Relational databases are typically used for data warehouses because they provide a structured and organized way to store and manage large volumes of data. NoSQL databases, data lakes, and cloud storage are also used for data warehousing, but they are typically used for different purposes or in conjunction with relational databases.

Aggregation is the process of creating a summary table from a larger table. It involves combining multiple rows of data into a single row, summarizing the data using functions such as SUM, COUNT, and AVERAGE.

Single-tier architecture is not a common data warehousing architecture. In a single-tier architecture, all components of the data warehouse, including the data storage, data processing, and user interface, are located on a single server. Two-tier, three-tier, and multi-tier architectures are more common in data warehousing.

Partitioning is the process of dividing a large table into smaller, more manageable tables. It is done to improve performance and scalability by distributing data across multiple storage devices or servers.

Microsoft SQL Server is not a common data warehousing tool. It is a relational database management system (RDBMS) that can be used for data warehousing, but it is not specifically designed for that purpose. Informatica PowerCenter, Talend Open Studio, and Pentaho Data Integration are all popular data warehousing tools.

OLAP (Online Analytical Processing) is the process of creating a multidimensional data structure for fast data retrieval. It is used for complex data analysis and reporting.

Scrum is not a common data warehousing methodology. It is an agile software development framework that is used to manage complex projects. Agile, Waterfall, and Spiral are all common data warehousing methodologies.

Physical design is the process of creating a physical data model from a logical data model. It involves selecting the appropriate data storage structures and technologies to implement the data warehouse.

Normalization is not a common data warehousing performance optimization technique. Normalization is a database design technique that involves organizing data into tables and columns to reduce data redundancy and improve data integrity. Indexing, partitioning, and caching are all common data warehousing performance optimization techniques.

Data loading is the process of loading data into a data warehouse. It involves transferring data from source systems to the data warehouse.

Data profiling is not a common data warehousing security measure. Data profiling is the process of analyzing data to understand its structure, content, and quality. Access control, encryption, and data masking are all common data warehousing security measures.