A multivalued attribute in database modeling is an attribute that can have multiple values for each entity. This concept is used to represent situations where an entity can have multiple values for a specific attribute, without creating a separate entity for each value.

Database tuning involves adjusting the physical design and configuration parameters of a database to optimize its performance. This includes optimizing queries, indexing, and other settings to ensure efficient data retrieval and processing. Tuning is an ongoing process to adapt the database to changing requirements and workload.

A Data Mart typically focuses on a specific functional area. Unlike a Data Warehouse, which covers the entire organization, a Data Mart is designed for a particular business unit or department's specific needs, providing more targeted insights.

Horizontal data partitioning involves dividing a table into rows based on a condition, allowing for efficient storage and retrieval of specific subsets of data. Vertical partitioning, on the other hand, involves dividing a table into columns, typically based on the frequency of access, reducing I/O overhead.

In SQL, the keyword used for aggregating data in a SELECT statement is SUM. It is used to calculate the sum of values in a particular column, providing a way to aggregate data and obtain meaningful insights from the dataset.

The Authorization model is designed to prevent unauthorized users from accessing or modifying data in a database. It involves defining and managing user privileges and permissions to ensure that only authorized users can perform specific actions on the database.

Data Marts typically have larger data volumes compared to the broader-scoped Data Warehouses. However, Data Marts have narrower specificity, focusing on a subset of data relevant to a specific business unit or department. This allows for more targeted and efficient analysis.

The process of decomposing a relation into smaller relations that meet normalization requirements is known as decomposition. It involves breaking down a table into smaller, more manageable tables to achieve higher normal forms and improve data integrity.

One advantage of using a Key-Value Store is its ability to handle write-intensive workloads efficiently. Key-Value Stores are optimized for simple read and write operations, making them well-suited for scenarios where write operations are frequent and need to be executed quickly.

In column-family stores, data storage occurs in a column-wise manner. Unlike traditional row-wise storage, where entire rows are stored together, column-family stores organize data by columns. This facilitates efficient retrieval of specific columnar data, making it suitable for scenarios where selective data retrieval is common.