In ETL (Extract, Transform, Load) testing, the "Extraction" phase involves retrieving data from various source systems, which could be databases, files, or other repositories. This phase focuses on efficiently and accurately extracting the required data without loss or corruption.

Historical data analysis with monitoring and profiling tools offers the key benefit of identifying performance trends. By analyzing historical data, one can recognize patterns, understand performance fluctuations over time, and make informed decisions about optimizing database performance. This process helps in proactive performance management and capacity planning.

The SQL JOIN clause is used to combine rows from two or more tables based on a related column between them. It allows you to retrieve data that spans across multiple tables, making it a powerful tool for querying data stored in a relational database management system (RDBMS).

One effective way to prevent SQL injection attacks in your applications is to use parameterized queries. Parameterized queries separate SQL code from user input, making it impossible for attackers to inject malicious SQL commands into input fields. By using placeholders for user input, parameterized queries ensure that user-supplied data is treated as data rather than executable code. Additionally, implementing input validation, using stored procedures, and employing web application firewalls are other strategies to mitigate the risk of SQL injection attacks.

ETL stands for Extract, Transform, Load. It is a crucial process in data warehousing and database testing where data is extracted from various sources, transformed according to business rules, and loaded into a target database or data warehouse for analysis and reporting purposes.

It's important to ensure that test data generation tools comply with data privacy regulations when handling sensitive information. Compliance with privacy regulations ensures that sensitive data is handled appropriately and securely during the testing process.

Database authorization is the process of granting or denying access to specific data based on the user's role and permissions. It ensures that only authorized users can access the data they are allowed to see, thus making option 3 the correct choice.

Authorization testing focuses on evaluating whether users have the necessary permissions and privileges to access specific resources or perform certain actions within the system.

Data reconciliation in data consistency testing refers to the process of identifying and resolving inconsistencies between different data sets. This involves comparing data from various sources to ensure alignment and accuracy, thus maintaining data integrity.

Upon encountering data corruption in the target system, the next step in the testing process should be to perform data validation. Data validation involves verifying the accuracy, completeness, and consistency of migrated data against predefined criteria and expectations. This step helps identify any discrepancies or anomalies caused by data corruption and ensures the integrity of financial data. Additionally, analyzing data transformation and loading processes can help pinpoint the source of corruption and prevent its recurrence in future migration attempts. Restoring data from backup may be necessary if the corruption is severe, but it should be complemented with thorough data validation to confirm the integrity of the restored data. Updating the data migration plan may also be required to incorporate lessons learned from the encounter with data corruption and improve future migration efforts.