Ensuring data integrity in a banking system involves implementing referential integrity constraints. This ensures that relationships between tables are maintained, preventing orphaned records and inconsistencies. Regular backups, while important, focus more on data recovery than on preventing integrity issues.

In Slowly Changing Dimensions (SCD), Type 2 involves creating a new record for each change. This method ensures that historical data is preserved, as each version of the record is stored with its effective start and end dates.

SQLiteStudio is a cost-effective solution known for its ease of use and flexibility. It is a lightweight tool suitable for startups with limited resources. It provides a user-friendly interface and supports various database management tasks, making it an ideal choice for small-scale projects.

Functional data partitioning divides data based on specific criteria or functions relevant to the application. This approach allows for tailored partitioning strategies that align with the application's logic, facilitating optimized data distribution and retrieval.

A Data Warehouse integrates data from multiple sources. This includes data from different departments, systems, and formats to provide a unified view for analytical purposes. The integration helps in obtaining a comprehensive and consistent view of the organization's data.

The key difference is that a primary key cannot have null values, ensuring each record is uniquely identified, while a unique key can have null values, allowing for some flexibility in data constraints.

A Document Store is well-suited for a recommendation engine in a streaming service. It allows storing and retrieving complex user data, such as viewing histories and preferences, in a flexible and scalable manner, enabling efficient analysis for personalized recommendations.

In a star schema, the relationship between fact and dimension tables is one-to-many. This means that for each record in the fact table (containing transactional data), there can be multiple related records in the dimension tables (containing descriptive attributes). This structure enables efficient querying and analysis of data in a data warehouse environment.

Aggregation improves query performance by reducing the amount of data processed. Instead of working with detailed records, aggregating data allows databases to handle summary values, which is more efficient for queries. This optimization becomes crucial, especially in large databases with extensive datasets.

All of the options are essential steps in performing effective Reverse Engineering. Analyzing existing data, documenting the structure, and interviewing knowledgeable personnel help in understanding and reconstructing the database schema.

Data Encryption is not a compression technique. While encryption is essential for securing data, it focuses on converting data into a secure format rather than reducing its size. Common compression techniques like Run-Length Encoding, Huffman Coding, and Lempel-Ziv-Welch aim to minimize data size for storage or transmission purposes.

A NoSQL database is a type of document-based database optimized for high-speed data retrieval and processing. NoSQL databases are non-relational and provide flexible schema designs, making them suitable for handling unstructured and semi-structured data efficiently.