20 Data Vault Interview Questions and Answers

Data Vault is a type of data warehouse that is used to store and manage large amounts of data. It is a popular choice for businesses because it is scalable and can be easily integrated with other data warehouses. If you are applying for a position that involves Data Vault, it is important to be prepared for the interview process. In this article, we will review some of the most common Data Vault interview questions and how you can answer them.

Data Vault Interview Questions and Answers

Here are 20 commonly asked Data Vault interview questions and answers to prepare you for your interview:

1. What is Data Vault?

Data Vault is a data modeling technique that is designed to provide a flexible and scalable approach to data warehousing. Data Vault models are composed of three types of tables: hubs, links, and satellites. Hubs are used to store information about entities, while links are used to store relationships between entities. Satellites are used to store information about the attributes of entities.

2. What are the main components of a data vault?

The main components of a data vault are the data store, the metadata store, and the data warehouse. The data store is where all of the raw data is kept. The metadata store is where information about the data is kept, such as who created it, when it was created, and what its purpose is. The data warehouse is where all of the processed data is kept.

3. Can you explain what a hub, link, and satellite are in the context of Data Vault?

A hub is a table in a Data Vault that contains a unique identifier for each record. A link is a table that connects two or more hubs together. A satellite is a table that contains additional information about a record in a hub.

4. How do you load data into the Data Vault?

There are a few different ways to load data into the Data Vault. One way is to use the Data Vault Loader, which is a tool specifically designed for loading data into the Data Vault. Another way is to use a ETL tool, such as Informatica or SSIS, to extract the data from the source, transform it into the appropriate format, and then load it into the Data Vault.

5. What’s the difference between a type 1 and type 2 change in the context of loading data into Data Vault?

A type 1 change is a change to the structure of the data, such as adding a new column to a table. A type 2 change is a change to the data itself, such as updating the value of a particular cell.

6. What is a business key? Why is it important?

A business key is a unique identifier for a piece of data within a business context. It is important because it allows data to be linked together across different systems and data sets, and it ensures that data can be accurately traced back to its source.

7. What is an operational data source?

An operational data source is a source of data that is used to support operational processes. This data is typically transactional in nature, and may be stored in a relational database, flat file, or some other type of data store.

8. Is it possible to create more than one fact table from a single dataset using Data Vault? If yes, then how?

Yes, it is possible to create multiple fact tables from a single dataset using Data Vault. This can be done by creating multiple hubs, each of which can be used to create a separate fact table.

9. Can you give me some examples of real-world applications that use Data Vault architecture?

Data Vault is a popular architecture for data warehouses and data lakes. It is used by companies such as Amazon, Facebook, and Google.

10. What does the staging area in Data Vault represent?

The staging area in Data Vault represents a temporary location where data is stored before it is processed and loaded into the Data Vault. This area is used to ensure that all data is cleansed and formatted correctly before it is entered into the Data Vault itself.

11. What are the benefits of using Data Vault over other architectures like star schema or snowflake schema?

Data Vault has a number of advantages over other data architectures. First, it is much more scalable than other architectures, so it can handle large amounts of data more effectively. Second, it is more flexible, so it can accommodate changes to the data more easily. Finally, it is more consistent, so it is easier to maintain over time.

12. What is the best way to manage changes in enterprise metadata with Data Vault?

The best way to manage changes in enterprise metadata with Data Vault is to use the Data Vault Change Management Framework. This framework provides a set of tools and processes that can be used to track, manage, and automate changes to enterprise metadata.

13. What happens if two records have the same hash value but different attributes? How can we make sure this doesn’t happen?

In the event that two records have the same hash value but different attributes, the data vault will simply store both records. There is no way to guarantee that this won’t happen, but it is not necessarily a problem. Having duplicate records in the data vault can simply be seen as a way of ensuring that all data is captured and stored.

14. What is a surrogate key? Why is it important when creating a Data Vault model?

A surrogate key is a unique identifier for a given row of data. It is important when creating a Data Vault model because it ensures that each row of data can be uniquely identified, even if the data itself changes. This is important because it allows for historical data to be tracked and compared, even as the data itself changes over time.

15. What is a primary key? Why is it important when creating a Data Vault model?

A primary key is a key that uniquely identifies a record in a table. It is important when creating a Data Vault model because it ensures that each record in the table can be uniquely identified. This is important for data integrity and for making sure that the data in the table can be accurately linked to other data in the Data Vault.

16. What are Type 0, Type 1, and Type 2 changes?

A Type 0 change is a change to the structure of the data, such as adding a new column to a table. A Type 1 change is a change to the data itself, such as updating the value of a particular cell. A Type 2 change is a change to the meaning of the data, such as changing the units of measurement.

17. What is a slowly changing dimension? Give me some examples of Type 2 changes in a data warehouse.

A slowly changing dimension is a type of data warehouse table that captures changes to data over time. This is typically accomplished by creating multiple versions of the same record, with each version representing the data as it existed at a specific point in time. Some examples of Type 2 changes that might be captured in a slowly changing dimension table include a change of address, a change in job title, or a change in marital status.

18. What is metadata management?

Metadata management is the process of organizing and managing data so that it can be easily accessed and used. This includes creating metadata standards, maintaining metadata repositories, and managing metadata changes.

19. What is a semantic layer?

A semantic layer is an abstraction layer that sits on top of a data warehouse. It is used to present the data in a way that is easy for users to understand and work with. The semantic layer defines the relationships between the different data elements in the warehouse, and provides a consistent interface for users to access the data.

20. How do you handle updates in Type 2 dimensions?

When a Type 2 dimension is updated, the changes are tracked in the history table. A new record is added to the history table, and the current record is updated to reflect the changes.