In the ETL process, the "Transform" step involves cleaning and transforming the extracted data to ensure it meets the quality and structure requirements of the target system before loading.

Establishing data lineage and traceability should be the primary focus to ensure compliance with international financial regulations. This involves documenting the origin and movement of data throughout its lifecycle, ensuring transparency and accountability in data handling processes, which is crucial for regulatory compliance.

Branching in version control systems is essential for managing multiple versions of test scripts effectively. It allows testers to work on separate branches, making changes without affecting the main codebase until ready for integration.

Automated regression testing in ETL is faster and more efficient compared to manual testing. Automation allows for quicker execution of test cases, enabling comprehensive regression testing in a shorter time frame.

For effective and secure extraction of patient data from legacy systems, Incremental Extraction strategy should be employed. This approach extracts only the data that has changed since the last extraction, reducing the load on systems and minimizing the risk of errors or data loss.

Cloud computing has impacted data integration in modern ETL processes by providing improved performance, cost reduction, and increased scalability. It offers flexibility and resources on-demand for efficient data processing.

Apache Spark offers a feature for real-time data processing and ETL operations. It is an open-source, distributed computing system that provides fast and general-purpose cluster-computing frameworks for big data processing.

The implementation of a test automation framework in ETL testing improves test coverage and efficiency. Automated tests can be executed more quickly and consistently, leading to better overall quality assurance.

Data Quality testing is conducted to ensure the accuracy and completeness of data. It involves validating data integrity, consistency, and conformity to predefined standards.

Stream processing in real-time data integration introduces the need to test data integrity in motion. Unlike traditional batch processing, where data is static, stream processing deals with data in motion, requiring tests to ensure data consistency, accuracy, and completeness as it flows through the system.

The role of a split transformation in ETL is to divide a single data stream into multiple streams based on specified criteria. This allows for parallel processing or routing of data to different destinations based on conditions such as value ranges, business rules, or destination targets.

A missing value in ETL testing is typically categorized as a Null Value. Null values represent the absence of data and are important to detect and handle during the testing process for accurate data processing.