20 Slowly Changing Dimensions Interview Questions and Answers

Slowly Changing Dimensions (SCD) are an important part of data warehousing and data modeling. They help organizations track changes in data over time, which can be useful for trend analysis and forecasting. When applying for a position that involves data warehousing or data modeling, you may be asked questions about SCDs. In this article, we review some of the most common SCD interview questions and provide guidance on how to answer them.

Slowly Changing Dimensions Interview Questions and Answers

Here are 20 commonly asked Slowly Changing Dimensions interview questions and answers to prepare you for your interview:

1. What is Slowly Changing Dimension?

Slowly Changing Dimension (SCD) is a term used in data warehousing that refers to changes in data that occur slowly over time. For example, a customer’s address or phone number might change slowly over time, while their name or gender would not change. In order to track these changes, data warehouses use SCD to create new records for each change, rather than overwriting the existing data. This allows for a complete history of the data to be maintained.

2. Can you explain the difference between a Type 1 and Type 2 SCD?

A Type 1 SCD is one where the original data is overwritten by new data. This is the simplest form of SCD, but it can lead to data loss if the new data is incorrect. A Type 2 SCD keeps both the original and new data, allowing for a history of changes to be maintained. This is more complex, but it prevents data loss.

3. Why are slowly changing dimensions needed in data warehousing?

Slowly changing dimensions are needed in data warehousing in order to track changes to data over time. For example, if you are tracking customer data, you may need to be able to see how that data has changed over time in order to better understand your customer base. Slowly changing dimensions allow you to do this by tracking changes to data over time and storing multiple versions of the data as needed.

4. What types of slowly changing dimensions exist?

There are three types of slowly changing dimensions: Type 1, Type 2, and Type 3. Type 1 dimensions simply overwrite old data with new data. Type 2 dimensions keep track of both the old and the new data, creating a new record each time there is a change. Type 3 dimensions keep track of both the old and the new data, but also keep track of when the change occurred, creating a new record each time there is a change and also creating a record that links the old and new data together.

5. How do you handle slowly changing dimensions when there are multiple sources for your data?

When you are dealing with slowly changing dimensions, it is important to have a process in place to track the changes that are happening. This way, you can ensure that your data is accurate and up to date. One way to do this is to create a separate table for each source of data. This way, you can track the changes that are happening in each table, and then merge the data together when necessary.

6. What’s the best way to resolve conflicting data from different source systems for an SCD?

There is no one-size-fits-all answer to this question, as the best way to resolve conflicting data from different source systems for an SCD will vary depending on the specific situation. However, some tips on how to resolve such conflicts may include using data cleansing techniques to standardize the data, using data reconciliation tools to identify and correct errors, and manually reviewing and editing the data as needed.

7. If we have one million records with 10 fields each, how long will it take to update an SCD table?

If we have one million records with 10 fields each, it will take approximately 1 second to update an SCD table.

8. What is a surrogate key and why are they used in slowly changing dimensions?

A surrogate key is a unique identifier for a row in a database table that is used to support slowly changing dimensions. The key is typically an auto-generated number that has no meaning outside of the database. The main reason for using a surrogate key is to avoid having to update primary keys when data in the table changes. This can be a big performance boost, especially in large tables.

9. What is a composite primary key? Is it possible to use them in SCDs?

A composite primary key is a key that is made up of two or more attributes. In the context of SCDs, a composite primary key can be used, but it is important to note that all of the attributes that make up the key must be included in the SCD.

10. What is versioning? When should it be used on SCDs?

Versioning is a process of tracking changes to data over time. When you version an SCD, you are essentially creating a new record each time the data changes, with each new record containing the date of the change. This allows you to track how the data has changed over time, which can be useful for auditing or trend analysis.

11. In what scenarios would you not want to use an SCD?

There are a few reasons why you might not want to use an SCD. One reason is if the data is not changing often enough to warrant the extra complexity. Another reason is if the data is not important enough to warrant the extra complexity. Finally, if you are not confident in your ability to manage the SCD, then it might not be the right choice for you.

12. What is the importance of natural keys in the context of SCDs?

In the context of SCDs, natural keys are important because they can be used to uniquely identify a record in the data warehouse. This is especially important when dealing with slowly changing dimensions, because it can be difficult to track changes to records over time if there is no way to uniquely identify them. Natural keys can help to solve this problem by providing a way to always identify a specific record, even as it changes over time.

13. What is the most efficient way to handle updates on historical information stored as part of an SCD?

The most efficient way to handle updates on historical information stored as part of an SCD is to use a Type 2 SCD. With a Type 2 SCD, every time a piece of information changes, a new record is created with the updated information. The old record is then marked as inactive. This allows you to keep a complete history of the data, while still only having to update a single record when something changes.

14. How can you determine the correct level of granularity for storing data as part of an SCD?

The level of granularity for storing data as part of an SCD is determined by the need for historical data. If you need to be able to track changes over time at a very granular level, then you will need to store more data. However, if you only need to track changes at a high level, then you can get away with storing less data.

15. Do all types of SCDs require maintaining a history of changes?

No, not all types of SCDs require maintaining a history of changes. For example, a Type 1 SCD will only ever have one record for each entity, so there is no need to maintain a history of changes. However, a Type 2 SCD will have multiple records for each entity, with each record representing a different point in time. In this case, it is necessary to maintain a history of changes in order to track how the entity has changed over time.

16. What is the best practice for managing errors encountered during the processing of an SCD?

The best practice for managing errors encountered during the processing of an SCD is to have a dedicated error-handling process in place. This process should be designed to capture all errors that occur, track them, and then report them back to the appropriate personnel. This will ensure that any errors that do occur are quickly identified and addressed, and that the data in the SCD is kept as accurate as possible.

17. What do you understand about change data capture?

Change data capture is a process that is used to track changes to data over time. This can be useful in a number of different situations, such as when you need to track how data has changed over time in order to understand trends or to monitor for changes that could indicate fraud.

18. How does CDC work in real-time streaming environments?

CDC is a process that is used to track changes to data over time. In a real-time streaming environment, CDC can be used to track changes to data as it is being processed. This allows for up-to-date information to be always available, which can be useful in a number of different situations.

19. What is the default behavior of Slowly Changing Dimensions?

The default behavior of Slowly Changing Dimensions is to keep track of changes to data over time, and to provide a history of those changes. This allows you to see how data has changed over time, and to track the history of those changes.

20. What happens if you don’t specify a type for a dimension?

If you don’t specify a type for a dimension, then it will be treated as a slowly changing dimension of type 2 by default. This means that new records will be added for new data, and existing records will be updated to reflect changes.