Data cleansing is about rectifying inconsistencies or anomalies in the data, ensuring data quality and integrity for accurate analysis and decision-making.

SQL injection attacks occur when an attacker inserts malicious SQL code into input fields, exploiting vulnerabilities in the application's SQL query construction. Escaping special characters is a technique used to neutralize the effect of these characters, preventing them from being interpreted as part of the SQL query. This helps to ensure the integrity and security of the database.

Regulatory standards for databases are typically defined by government agencies. These standards may vary depending on the industry and location, and organizations must ensure compliance with the relevant regulations to avoid legal and financial consequences.

ETL testing tools often include data profiling capabilities, allowing testers to analyze the data and identify potential quality issues such as inconsistencies, duplicates, or missing values. Profiling helps in understanding the data distribution and its characteristics, facilitating effective testing and validation of ETL processes.

SQL injection testing involves probing a system with malicious SQL queries to identify vulnerabilities. It is critical in SQL query testing to prevent attackers from exploiting vulnerabilities to gain unauthorized access to databases or manipulate data.

In ETL (Extract, Transform, Load) testing, ensuring the completeness of data is crucial. This involves verifying that all expected data is extracted, transformed, and loaded accurately.

Stress testing, a component of scalability testing, aims to determine the system's robustness and its ability to handle extreme conditions beyond its normal operational capacity. By subjecting the system to high loads or excessive stress, it helps identify potential bottlenecks, weak points, or performance limitations. This allows developers to optimize the system's performance and ensure its scalability under stressful conditions.

Data Quality Testing is required in this scenario to ensure that the data transformations are working correctly and that the data being loaded into the warehouse meets the expected quality standards. This involves validating data accuracy, completeness, consistency, and integrity throughout the ETL process. By performing comprehensive data quality tests, you can identify and rectify issues related to incorrect data transformations, ensuring the accuracy and reliability of the data in the data warehouse.

Common signs of SQL injection vulnerability include the presence of unsanitized user input directly concatenated into SQL queries, error messages that reveal database structure or query information, and the absence of input validation mechanisms. These indicators suggest that the system does not adequately sanitize and validate user input, making it vulnerable to SQL injection attacks.

In a blind SQL injection attack, attackers may exploit error messages or use comments to gather information. However, a common technique is using database metadata to understand the database structure.