20 Star Schema Interview Questions and Answers

Star schema is a type of database schema that is used in data warehouses. It is called a star schema because the entity-relationship diagram resembles a star, with a central table that contains the data, and a series of satellite tables that contain the associated data. When applying for a position that involves working with data warehouses, it is likely that you will be asked questions about star schema. In this article, we review some of the most common questions and how you should answer them.

Star Schema Interview Questions and Answers

Here are 20 commonly asked Star Schema interview questions and answers to prepare you for your interview:

1. What are the main elements of a star schema?

The main elements of a star schema are the fact table and the dimension tables. The fact table contains the data that will be analyzed, and the dimension tables contain the data that will be used to categorize the data in the fact table.

2. Can you give me some examples of typical dimensions in a star schema?

In a star schema, dimensions are typically the characteristics of the data being analyzed. For example, in a star schema for analyzing sales data, dimensions might include things like product, customer, time, and location.

3. How is a snowflake schema different from a star schema?

A snowflake schema is a type of star schema that is normalized in order to reduce redundancy. This means that some of the dimensions in the schema are represented by multiple tables, which are then linked together. A star schema, on the other hand, has all of its dimensions represented by a single table.

4. When would you use a star or snowflake schema?

A star schema is the simplest type of data warehouse schema, and is used when there is a single, central fact table with no more than a few hundred columns. A snowflake schema is more complex, and is used when there are multiple, linked fact tables.

5. Why do we need to define grain for a fact table?

The grain of a fact table defines the level of detail that is captured in the table. For example, a fact table tracking sales could have a grain of one row per sale, or it could have a grain of one row per product sold. The grain of a fact table should be chosen based on the needs of the business and the questions that the data in the table is meant to answer.

6. Can you explain what surrogate keys are and how they relate to star schemas?

A surrogate key is a unique identifier for a row in a database table. In a star schema, the surrogate key is typically used as the primary key for the fact table. The surrogate key allows the fact table to be linked to multiple dimension tables, without creating duplicate rows.

7. How can you ensure that updates to dimension tables don’t affect existing queries?

When you update a dimension table, you need to make sure that the changes don’t affect existing queries. The best way to do this is to use a star schema. A star schema is a type of database schema that is designed to optimize query performance. It does this by organizing data into a central fact table with associated dimension tables. This structure allows you to easily update dimension tables without affecting the fact table or the queries that rely on it.

8. Can you give me an example of a slowly changing dimension?

A slowly changing dimension is a type of dimension that changes slowly over time, as opposed to a rapidly changing dimension. An example of a slowly changing dimension would be a customer’s address. The customer’s address might change slowly over time, as they move from one house to another. In contrast, an example of a rapidly changing dimension would be a stock price, which can change rapidly and frequently.

9. What’s your opinion on using multiple fact tables within a single data warehouse?

I believe that multiple fact tables can be useful in certain situations, but they should be used sparingly. In general, I think it is best to stick to a single fact table per data warehouse. Having multiple fact tables can make it more difficult to query the data and can also lead to data duplication.

10. Can you show me an example where it makes sense to use more than one fact table?

In a data warehouse, it is common to have multiple fact tables that share dimensions. For example, you might have a fact table for sales data and another for inventory data. Both of these tables would share dimensions such as product, time, and location. This allows users to easily compare data from both tables side-by-side.

11. How does the amount of traffic to a web site factor into determining whether to use a star or snowflake schema?

The amount of traffic to a web site can be a factor in determining whether to use a star or snowflake schema. If the site gets a lot of traffic, then a star schema may be more appropriate because it can more easily handle large amounts of data. If the site gets less traffic, then a snowflake schema may be more appropriate because it can provide more flexibility in terms of the data that is stored.

12. What’s the difference between a degenerate and de-normalized dimension?

A degenerate dimension is one where the dimension key is also an attribute of the fact table. This can happen when, for example, you have a fact table that tracks sales and the dimension key is the sales order number. A de-normalized dimension is one where the attributes of the dimension are stored in the fact table. This can happen when, for example, you have a fact table that tracks sales and the attributes of the sales (such as the customer name, product name, etc.) are all stored in the fact table.

13. Is it possible to have multiple fact tables in a star schema? If yes, then why would it be required?

Yes, it is possible to have multiple fact tables in a star schema. This would be required if you wanted to track different types of information in separate tables. For example, you could have a fact table for sales data and another for inventory data. This would allow you to more easily query and analyze the data separately.

14. What is the best way to handle duplicate rows while loading data into a star schema?

The best way to handle duplicate rows while loading data into a star schema is to use a primary key. A primary key is a unique identifier for each row in a table. By using a primary key, you can ensure that each row in the table is unique and that there are no duplicate rows.

15. What measures should be taken to speed up processing time when querying large data sets stored in a star schema?

When querying large data sets stored in a star schema, the following measures should be taken to speed up processing time:

1. Use a clustered index on the Fact table.
2. Use indexed views on the Dimension tables.
3. Use partitioning on the Fact and Dimension tables.
4. Use a data warehouse appliance if possible.

16. What are the advantages of using star schemas over third-normal form schemas?

The main advantage of using star schemas is that they are much easier to query and understand than third-normal form schemas. This is because all of the information for a given dimension is stored in a single table, rather than being spread out across multiple tables. This makes it much simpler to write queries that pull data from multiple dimensions, since all of the data is in one place. Additionally, star schemas tend to be more efficient than third-normal form schemas since there is less data redundancy.

17. What tools do you recommend using when designing a star schema?

When designing a star schema, it is important to use a tool that will allow you to easily visualize the data. One tool that can be used for this purpose is Microsoft Visio. This tool allows you to create a diagram of the star schema, which can be helpful in understanding the relationships between the different entities.

18. What is a conformed dimension?

A conformed dimension is a dimension that has been designed to be compatible with multiple data sources. This means that the dimension can be used in multiple data warehouses or data marts without having to be changed or modified. This compatibility is achieved through the use of a common data element or set of data elements that can be mapped to the various data sources.

19. How is an aggregate fact table different from a simple fact table?

An aggregate fact table is a fact table that contains pre-calculated aggregations, such as sums or averages. A simple fact table, on the other hand, just contains the raw data that will be used to calculate the aggregations.

20. What are bridge tables?

Bridge tables are used to connect two or more star schemas. They contain the keys from each of the star schemas that are being connected, and can be used to query data from multiple star schemas at the same time.