Data governance in ETL plays a crucial role in ensuring data quality and compliance. It involves defining and implementing policies to maintain data integrity throughout the ETL process.

When adopting an ETL tool with AI for data quality improvement, key factors include integration with existing systems, scalability to handle large datasets, AI model performance, and ensuring robust data security measures are in place. Integration and scalability are crucial for a seamless transition, while AI model performance and data security are essential for effective data quality improvement.

White-box testing focuses on the internal structure of the software. Test cases are designed with knowledge of the internal code, allowing for thorough coverage of different code paths and logic.

The primary purpose of using metrics and Key Performance Indicators (KPIs) in ETL testing is to measure the performance of the ETL process. Metrics and KPIs help in assessing the efficiency, accuracy, and reliability of data extraction, transformation, and loading activities. They provide insights into the quality of data and the effectiveness of the testing process.

Talend's open-source nature enhances adaptability and integration with other technologies. The community contributions and open-source model allow for flexibility, customization, and collaboration, making it adaptable to various environments.

Stress testing is vital in a data migration project to assess the ETL system's performance under extreme conditions. It helps identify potential failure points, ensuring minimal downtime and maintaining data integrity during high-stress situations.

Latency is a key factor to test in terms of data flow in real-time data integration. It involves the time delay between data being produced and consumed, ensuring that real-time processing meets the required speed and responsiveness.

In the future, Automation tools are likely to become more prevalent in ETL testing for efficiency and accuracy. Automation can streamline repetitive tasks, reduce manual errors, and enhance the overall testing process in the context of ETL.

Handling large data volumes is a major challenge in automated ETL testing. The automated process needs to efficiently manage and validate substantial amounts of data, which can be resource-intensive.

Data Subsetting plays a crucial role in Test Data Management when testing distributed data systems. It involves creating a subset of data relevant to the test cases, optimizing storage and improving test efficiency.