Data Federation simplifies BI tool integration by providing a unified view of data from different sources. It eases data access and analysis, reducing the complexity associated with integrating diverse datasets.

Throughput is a crucial metric in performance testing of data pipelines. It measures the rate at which data can be processed and transmitted, providing insights into the system's efficiency and capacity.

In ETL testing, a data anomaly refers to a Data Discrepancy, which indicates differences or irregularities in the data between the source and target systems. Identifying and resolving such anomalies is crucial for ensuring data accuracy and integrity.

Implementing Auto Scaling in the test environment allows for dynamic scaling and testing of ETL processes. This ensures optimal resource utilization during varying workloads.

Parallel loading strategy in data loading is beneficial for reducing load times and system impact. It involves dividing the data into smaller subsets and loading them concurrently, improving overall efficiency.

Machine Learning technology in AI can predict future data trends in ETL testing. It utilizes algorithms to analyze historical data, identify patterns, and make predictions about future trends, enhancing the testing process.

The process of continuously refining and improving test cases in Agile ETL testing is known as Test Case Refactoring. It ensures that test cases stay relevant and effective as the project evolves.

To retrieve a specific set of records in SQL, the SELECT statement is used with conditions specified in the WHERE clause. This allows filtering based on certain criteria.

Unique constraints in SQL ensure that all values in a column are different. This is commonly used to enforce uniqueness in a specific column, such as a unique username in a user table.

A lookup transformation in data transformation is used for matching and retrieving related data from another table. It helps enrich the data by incorporating additional information based on matching keys.