Scenario: An e-commerce platform processes orders and handles inventory management. What steps would you implement to enforce data integrity in this system?
- Allowing inconsistent data formats
- Delayed data validation processes
- Redundancy in data storage
- Using unique identifiers for products and orders
Enforcing data integrity in an e-commerce platform involves using unique identifiers for products and orders. This ensures that each product and order is uniquely identified, preventing duplication and ensuring consistency. Redundancy, inconsistent data formats, and delayed validation processes can introduce errors and compromise data integrity.
What does the term "weak entity" refer to in an ERD?
- An entity that depends on another entity for identification
- An entity that does not have a unique identifier
- An entity with a composite primary key
- An entity without any attributes
In an ERD, a weak entity is one that does not have a unique identifier on its own and depends on another related entity for identification. It typically has a partial key, which is a set of attributes that, in combination with the parent entity's key, uniquely identifies instances of the weak entity.
How does eventual consistency differ from strong consistency in NoSQL databases?
- Data consistency is achieved over time and may not be immediate
- Data consistency is achieved through strict synchronization
- Data consistency is irrelevant in NoSQL databases
- Data is immediately consistent across all nodes
Eventual consistency in NoSQL databases means that over time, all replicas of the data will converge to the same value, but it may not be immediately consistent. Strong consistency, on the other hand, ensures that all nodes have the same data at the same time.
How does a graph database differ from other types of NoSQL databases?
- It focuses on relationships between data points
- It is optimized for high-speed transactions
- It supports complex queries with SQL
- It uses a tabular structure to store data
A graph database differs by focusing on relationships between data points. It utilizes nodes and edges to represent entities and their connections, making it particularly suitable for scenarios where understanding and querying relationships are crucial, such as social networks or fraud detection.
The relationship between a department and an employee in a company database is typically _______.
- Many-to-Many
- Many-to-One
- One-to-Many
- One-to-One
The relationship between a department and an employee in a company database is typically a One-to-Many relationship. This indicates that one department can have multiple employees, but each employee belongs to only one department.
Which of the following statements best describes the use of SQL in data modeling?
- SQL is exclusively used for creating ER diagrams
- SQL is only applicable in NoSQL databases
- SQL is primarily used for designing user interfaces
- SQL is used for querying and manipulating data in relational databases
SQL is commonly used for querying and manipulating data in relational databases. It allows for the creation, modification, and retrieval of data, making it a crucial tool in the data modeling process. SQL is not limited to just data definition but extends to data manipulation as well.
Scenario: Two data modelers are working on the same entity-relationship diagram (ERD) simultaneously. They make conflicting changes to the diagram. How would you resolve this conflict?
- Ask a senior data modeler to make the decision
- Choose one version and discard the other
- Consult with both modelers to reach a consensus
- Keep both versions and merge changes manually
When conflicts arise between data modelers working on the same ERD, the best approach is to consult with both modelers to reach a consensus. This ensures that both perspectives are considered, and a mutually agreeable solution can be reached, promoting collaboration and avoiding potential disputes.
Scenario: A retail chain wants to analyze sales data from each of its store locations separately to identify regional trends and preferences. Would you recommend implementing a Data Warehouse or multiple Data Marts, and what factors would you consider in making this decision?
- Both Data Warehouse and Multiple Data Marts
- Data Warehouse
- Multiple Data Marts
- Neither Data Warehouse nor Multiple Data Marts
For the retail chain analyzing store-specific data, implementing multiple Data Marts is recommended. Data Marts are subsets of a Data Warehouse, focusing on specific business areas. This approach allows for tailored analysis of individual store data while maintaining the benefits of centralized data management. It provides a balance between centralized control and local flexibility.
Which schema typically results in simpler queries: Star Schema or Snowflake Schema?
- Both have the same query complexity
- Query complexity depends on data size
- Snowflake Schema
- Star Schema
Star Schemas typically result in simpler queries. The denormalized structure of a Star Schema, with a central Fact Table surrounded by Dimension Tables, reduces the number of joins needed in queries. This simplicity makes it easier for users to retrieve and analyze data without navigating through multiple normalized tables as in a Snowflake Schema.
Scenario: You are designing a social networking platform where users can follow other users and share posts. Which NoSQL database type would you choose and why?
- Column-family Store
- Document Store
- Graph Database
- Key-Value Store
For a social networking platform with complex relationships like followers and shared posts, a Graph Database would be suitable. Graph databases excel at handling relationships between entities, making it efficient to represent connections between users and their activities in this scenario.