When dealing with large data volumes in ETL, employing parallel processing is a key optimization strategy. This involves dividing the data processing tasks into parallel threads, significantly reducing the overall processing time and enhancing efficiency.

The Inconsistency Data Anomaly occurs when there are inconsistencies in data across different sources. ETL testing aims to identify and rectify such inconsistencies to ensure data integrity and reliability.

The Response Viewer feature in Postman provides a detailed breakdown of API responses, facilitating easier analysis during testing. Testers can view the complete response, including headers and body, in a structured manner. This enhances the understanding of API behavior and aids in identifying issues or discrepancies in the response, improving the testing process.

Test scenarios are derived from user stories and requirements, providing a basis for creating detailed test cases that cover various aspects of the software functionality.

The Global Variables feature in Postman enables testers to set up variables globally, making them accessible across multiple requests within a collection. This capability is valuable for managing dynamic data, such as authentication tokens or dynamic URLs, consistently throughout the entire collection of API requests.

Selenium Grid, combined with TestNG annotations, is a robust approach for implementing parallel testing across multiple browsers. Selenium Grid allows the distribution of test execution across different nodes, each running a specific browser, while TestNG annotations facilitate easy parallel test execution. This approach improves testing efficiency and reduces the overall execution time for a test suite.

Risk analysis in functional testing aids in identifying critical areas and potential risks in the software, which helps in determining test scenarios and prioritizing testing efforts accordingly.

UFT, or Unified Functional Testing, is primarily used for Test Automation. It allows testers to automate the execution of functional and regression test cases, enhancing efficiency and repeatability in the testing process.

Load testing is an example of non-functional testing. It evaluates how the system performs under specific conditions, such as heavy loads or concurrent users.

"Test progress" in test monitoring and control refers to assessing the overall progress of the testing phase within the project. It involves tracking the completion of test design, execution, and defect resolution to ensure that testing aligns with project timelines and objectives.

The integration of Big Data technologies is expected to accelerate data processing in ETL testing. Big Data tools enable the processing of large volumes of data quickly, impacting the speed and efficiency of ETL testing processes.

The first step in the Test Execution Lifecycle is Test Planning. This phase involves defining the overall testing strategy, objectives, scope, and resources required for the testing effort. It sets the foundation for the subsequent testing phases.