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.

Authorization ensures that only authorized users can access specific data within a database. Authentication verifies the identity of users before granting access rights.

When a previously passing test script fails unexpectedly, verifying recent changes in the application code or database schema is crucial. Any modifications or updates could have introduced errors or inconsistencies, leading to the failure. By reviewing the changes, developers and testers can pinpoint potential causes and take corrective actions promptly. This ensures the stability and reliability of the test scripts and the overall application.

Intermittent test failures in database scripts could result from data dependencies or conflicts arising from concurrent test executions. When multiple tests manipulate the same data simultaneously, it can lead to inconsistent outcomes, causing intermittent failures. To troubleshoot this issue, identifying and resolving data dependencies, ensuring proper synchronization between test steps and transactions, and implementing mechanisms to manage concurrent access to shared data are essential steps. This ensures test scripts execute reliably and produce consistent results.

Handling large data sets in a database often poses the challenge of scalability, where traditional database systems struggle to efficiently manage and process vast amounts of data. Scalability refers to the ability of a system to handle increasing amounts of workload or data without compromising performance or responsiveness.

The keyword commonly used in SQL to specify the order in which the result set should be returned is ORDER. This keyword is used in conjunction with ORDER BY clause in SQL queries to sort the result set based on one or more columns. By specifying the order, the database engine can efficiently retrieve and return the data in the requested sequence, potentially improving query performance.

NoSQL databases are often used for distributed data storage and retrieval in big data scenarios. Unlike traditional relational databases, NoSQL databases are designed to handle large volumes of unstructured or semi-structured data across distributed systems. They offer flexible data models, horizontal scalability, and high availability, making them well-suited for handling the complexities of big data environments. Examples of NoSQL databases include MongoDB, Cassandra, and HBase.