The key distinction lies in the presence of a primary key. A strong entity always has a primary key, while a weak entity relies on a partial key and requires the association with a strong entity for identity. Weak entities lack independence and rely on a strong entity for survival.

ER diagram tools facilitate team collaboration by providing role-based access to different team members. This ensures that each team member has the appropriate level of access and control, promoting effective collaboration in large-scale database projects.

One of the key features of column-family stores is their ability to handle mixed (read/write) workloads efficiently. This makes them suitable for applications with diverse and dynamic usage patterns, such as those in the realm of big data analytics and real-time data processing.

Data partitioning can improve scalability by reducing contention, allowing for parallel processing. Additionally, it can enhance fault tolerance by isolating failures to specific partitions, preventing system-wide failures. Understanding these effects is crucial for designing robust distributed systems.

In UML, an object represents an instance of a class. A class is a blueprint for objects, and instances of a class are created as objects. Objects encapsulate data and behavior defined by the class, providing a way to represent real-world entities in a system.

The technique used to combine multiple rows of data into a single row is called aggregation. This often involves using aggregate functions like SUM, AVG, COUNT, etc., to consolidate data and provide a more concise representation of information.

A recursive relationship is one where an entity can be related to itself through a recursive association. This type of relationship is common in scenarios where an entity has a hierarchical or tree-like structure.

NoSQL databases are specifically designed to scale horizontally, allowing them to handle increased loads by adding more servers to the database system. This makes them well-suited for applications with high scalability requirements, such as those experiencing rapid growth in data volume and user traffic.

In a column-family store, each column can have its own data type. Unlike traditional relational databases, column-family stores allow flexibility in the data types within a column, providing versatility in handling various types of information efficiently.

Boyce-Codd Normal Form (BCNF) ensures that every determinant in a relation is a candidate key. This form goes beyond 3NF and addresses certain types of dependencies, making the database more robust and minimizing redundancy.

One of the potential drawbacks of denormalization is increased data redundancy. While it can enhance read performance, it comes at the cost of redundant data, which can lead to higher storage requirements and potential data inconsistency.

While indexes enhance query performance, they come with drawbacks like increased storage space and maintenance overhead. Additional storage is required for index structures, and regular maintenance is needed to keep them optimized.