Comparing data at each ETL stage involves comparing the data extracted from source systems with the data at various stages of transformation and loading in the ETL process. This helps identify discrepancies and pinpoint where data integrity issues may have occurred. By systematically analyzing the data flow and transformations at each stage, testers can identify the root cause of the discrepancy and take appropriate corrective actions to ensure data consistency between source and target systems.

Denormalization is a common solution for handling large data sets efficiently. It involves intentionally introducing redundancy into a database design to improve read performance by reducing the need for joins and queries across multiple tables, at the expense of increased storage requirements and potential update anomalies.

Query performance testing involves assessing the efficiency and speed of SQL queries under various conditions. It contributes to SQL query optimization by identifying bottlenecks and areas for improvement, such as indexing strategies or query restructuring.

Database testing primarily focuses on ensuring data integrity, which means data is accurate, consistent, and reliable. This involves validating data storage, retrieval, and manipulation processes to ensure they meet the requirements.

Blind SQL injection doesn't rely on visible error messages from the database server to determine whether the injection was successful, whereas classic SQL injection relies on such error messages.

Data encryption is a method used to protect data at rest by converting it into ciphertext, making it unreadable without the appropriate decryption key. This encryption method is crucial for maintaining the confidentiality and integrity of sensitive data stored in databases.

The RAISEERROR function in SQL is used to generate a custom error message and to initiate error processing for the session. It allows developers to raise user-defined error messages with a specified error number and severity level. This is helpful in handling exceptional conditions and providing meaningful error messages to users or applications.

In the context of data processing, "ETL" stands for Extraction, Transformation, and Loading. This process involves extracting data from various sources, transforming it into a suitable format, and then loading it into a target destination such as a data warehouse or database. Extraction involves gathering data from different sources, Transformation involves converting the extracted data into a suitable format for analysis, and Loading involves transferring the transformed data into a target database or data warehouse.

Establishing a test environment that closely resembles the production environment is essential because it allows for accurate simulation of real-world conditions. This helps in identifying potential issues that might arise in the production environment, leading to more reliable test results and minimizing discrepancies between testing and production.

Composite indexes, involving multiple columns, can efficiently handle queries with complex filtering conditions or involving joins. By storing the relevant columns together in the index, it reduces the need for accessing the main table, thereby improving query performance.