Normalization
Q: What is the purpose of database normalization? Provide an example of how normalization can eliminate data anomalies.
A: The purpose of database normalization is to organize data in a database efficiently, eliminate redundancy, and ensure data integrity. By breaking down data into multiple tables and applying normalization rules, we can minimize data duplication and inconsistencies.
For example, let's consider a "Customer" table with columns such as CustomerID, CustomerName, and CustomerAddress. If we store both the shipping address and billing address for each customer in the same table, there might be instances where a customer's address changes, leading to multiple records with inconsistent data. By normalizing the database and creating separate "Customer" and "Address" tables linked by a foreign key, we can avoid data duplication and ensure that each address change is reflected consistently across all related records.
SQL Queries
Q: Write an SQL query to retrieve all employees who earn a salary higher than $50,000 from the "Employees" table. Include an explanation of the query.
A: The SQL query to retrieve all employees earning a salary higher than $50,000 from the "Employees" table is as follows: sql
SELECT * FROM Employees WHERE Salary > 50000;
In this query, we use the SELECT statement to specify the columns we want to retrieve from the "Employees" table. The asterisk (*) represents all columns. The FROM clause specifies the table name ("Employees"), and the WHERE clause filters the results based on the condition "Salary > 50000", returning only those employees who earn a salary higher than $50,000.
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Indexing
Q: Consider a table with 100,000 records. If the table has an index on a particular column, and you need to find a specific record using that column, how many comparisons would be required in a linear search versus a binary search? Explain the difference.
A: In a linear search, you would need to compare each record in the table sequentially until you find the desired record. Since there are 100,000 records, on average, you would need to make approximately 50,000 comparisons.
On the other hand, if the table has an index on the column you are searching, a binary search algorithm can be used. A binary search involves repeatedly dividing the search space in half until the desired record is found. Since the table has 100,000 records, log2(100,000) ≈ 16 comparisons would be required to locate the specific record using binary search.
The difference in the number of comparisons illustrates the advantage of indexing and binary search over linear search when dealing with large datasets. By reducing the number of comparisons required, indexing and binary search offer significant performance improvements, especially in scenarios with extensive data retrieval and search operations.
Database Design
Q: What is an Entity-Relationship (ER) diagram, and why is it used in database design? Explain the components and significance of an ER diagram.
A: An Entity-Relationship (ER) diagram is a visual representation of the relationships between entities in a database. It helps in database design by providing a clear understanding of the database structure, entities (objects or things), attributes (properties of entities), and the relationships (associations) among entities.
Components of an ER diagram include entities, attributes, and relationships. Entities represent real-world objects or concepts, such as "Customer" or "Product." Attributes describe the properties or characteristics of entities, such as "CustomerID" or "ProductName." Relationships indicate how entities are connected or associated with each other, such as "one-to-many," "many-to-many," or "one-to-one" relationships.
The significance of an ER diagram lies in its ability to represent the database schema visually, aiding in effective communication between stakeholders involved in the database design process. ER diagrams help identify entity relationships, define primary keys, determine cardinality between entities, and ultimately assist in the creation of an optimized, well-structured database.