In a logical model, the relationship between two entities where one occurrence of entity A can relate to many occurrences of entity B, and vice versa, is termed as _______.
- Many-to-Many Relationship
- Many-to-One Relationship
- One-to-Many Relationship
- One-to-One Relationship
In a logical model, a many-to-many relationship represents a situation where one occurrence of entity A can be associated with many occurrences of entity B, and vice versa. This is typically used to model complex relationships between entities.
A pharmaceutical company has data stored in various formats - Excel sheets, cloud databases, and on-premises SQL servers. They want a unified view of all this data for analysis. What should they consider implementing?
- Data Cleansing
- Data Extraction
- Data Virtualization
- Data Warehousing
To achieve a unified view of data from various sources, the pharmaceutical company should consider implementing a Data Warehousing solution. Data Warehousing involves the process of centralizing, storing, and organizing data from disparate sources into a structured repository, making it accessible for analysis and reporting.
A _______ is a subset of a data warehouse that focuses on a particular subject or department like sales or finance.
- Data Cube
- Data Mart
- Data Repository
- Data Silo
A "Data Mart" is a subset of a data warehouse that focuses on a specific subject or department, such as sales, finance, or a particular area of an organization. It contains data relevant to a particular business unit or group, making it easier to access and analyze data related to specific needs.
What is the main advantage of columnar databases when it comes to analytics and querying?
- Complex data relationships
- Fast data insertion
- High data storage efficiency
- Speed in aggregating and querying large datasets
The primary advantage of columnar databases for analytics and querying is their ability to quickly aggregate and query large datasets. Columnar databases store data in a column-wise fashion, which is more suitable for analytical workloads as it allows for faster data retrieval and processing for tasks like aggregation and reporting.
An e-commerce company wants a system that can handle millions of transactions per day, with quick read and write capabilities and consistency. What kind of database system would be most suitable?
- Columnar Database
- Document Database
- Key-Value Store
- NewSQL Database
To handle a high volume of transactions with quick read and write capabilities and consistency, a NewSQL database is the most appropriate choice. NewSQL databases combine the benefits of SQL (consistency) and NoSQL (scalability and speed) databases.
Why might a database administrator choose to denormalize a database?
- To optimize data storage and retrieval performance
- To reduce data redundancy and improve data consistency
- To reduce redundancy and improve data consistency
- To simplify the database structure and improve data integrity
A database administrator may choose to denormalize a database to optimize data storage and retrieval performance. Denormalization involves reducing the number of tables and increasing redundancy, which can speed up query performance, particularly in data warehousing where complex queries are common. However, it may come at the cost of some data integrity and consistency.
In a sales data model, which hierarchy is most likely to be used to analyze sales trends?
- Customer Hierarchy
- Location Hierarchy
- Product Hierarchy
- Time Hierarchy
In a sales data model, the Time Hierarchy is crucial for analyzing sales trends. It allows analysts to explore sales data over different time periods, such as daily, monthly, or yearly, to identify patterns, seasonality, and trends. This hierarchy helps in time-based analysis, forecasting, and decision-making.
In a top-down approach to building a data infrastructure, which is typically built first?
- Data Integration
- Data Marts
- Data Sources
- Data Warehouses
In a top-down approach to building a data infrastructure, data sources are typically the first components to be addressed. Data sources include various systems and databases that store raw data, and they need to be integrated and processed to feed into data warehouses and data marts. Starting with data sources is fundamental to ensuring data quality and consistency.
The process of cleaning and enhancing the data so it can be loaded into a data warehouse is known as what?
- Data Extraction
- Data Integration
- Data Loading
- Data Transformation
The process of cleaning, transforming, and enhancing the data to prepare it for loading into a data warehouse is called "Data Transformation." During this phase, data is cleansed, structured, and enriched to ensure its quality and consistency for analysis.
A strategy that involves making copies of the data warehouse at regular intervals to minimize data loss in case of failures is known as _______.
- Data Cleansing
- Data Erosion
- Data Purging
- Data Replication
Data replication is a strategy in data warehousing that involves creating copies of the data warehouse at regular intervals. This approach helps minimize data loss in case of failures by ensuring that there are up-to-date backup copies of the data readily available. Data replication is essential for data resilience and disaster recovery.