15 Data Warehouse Interview Questions and Answers

Written by Coursera Staff • Updated on

Considering a data warehouse job? Discover 15 common data warehouse interview questions, including example responses and alternate questions.

[Featured Image] A man is on a computer studying interview questions for a data warehouse job.

As companies increasingly rely on data to drive decisions, several exciting job opportunities in data warehousing have emerged. To start your preparation for an upcoming interview, consider practising your answers to common data warehouse interview questions. 

While each employer is likely to put their spin on common questions, practising how to relay your technical knowledge and industry-related experience can help you answer questions in a way that showcases your skill set.  

Overview of careers

Two common positions in data warehouse careers are data warehouse architect and data warehouse analyst. Data warehouse architects design and maintain their company's data warehouse. Depending on their employers' needs, they develop the data warehouse to provide specific insights to help them in decision-making. 

Data warehouse analysts analyse the information stored in the data warehouse for their company to provide clear information and insights. Another title used for similar roles is data warehouse engineer. Data warehouse engineers often work with the back end of data warehouse architectures to tailor database systems to company needs. 

As data warehouses are a relatively new type of technology, the precise responsibilities of each role will vary slightly from company to company. When preparing for an interview, pay close attention to the job description.

Data warehouse interview questions

This section will cover many commonly asked questions during data warehouse job interviews and their correct answers. Proper preparation is key for showcasing your knowledge and confidence during a job interview, so take the time to rehearse how you would answer each question.

1. What is the difference between a data warehouse and a database?

Why they’re asking this: The interviewer wants to test your basic knowledge of data warehouses and how they differ from existing data structures.

How to answer: Technically, a data warehouse is a type of database. Generally, databases store current information, whereas the design of a data warehouse allows it to analyse and model large, historical data. Data warehouses take the data from multiple databases and comprehensively analyse the information all in one place. 

Similar questions the interviewer may ask:

  • What is a data warehouse?

  • What is a database?

2. What are the components of data warehouse architecture?

Why they’re asking this: The interviewer will use this opportunity to test the depth of your understanding of the structure of a data warehouse.

How to answer: The main components of data warehouse architecture are the central database, ETL tools, metadata, query tools, and reporting layer.

Similar questions the interviewer may ask:

  • What does each component of a data warehouse do? 

  • Describe the architecture of a data warehouse.

3. How do OLTP and OLAP differ?

Why they’re asking this: While these are both processing systems, OLTP and OLAP tackle different types of data problems. Knowing which one to use and when will likely be important to your role. 

How to answer: Online transaction processing (OLTP)   helps with real-time transactions, whereas online analysing processing (OLAP) is analytical. OLAP is more complex as it takes historical data from multiple sources to perform multidimensional tasks. On the other hand, OLTP takes transactional data from a single source and can only perform simple queries. 

Similar questions the interviewer may ask:

  • Describe OLTP and its uses.

  • Describe OLAP and its uses.

4. Define aggregate tables.

Why they’re asking this: Aggregate tables are important when it comes to optimising large-scale performance.

How to answer: Aggregate tables provide a higher-level summary of the data stored in the warehouse. These tables are beneficial because they can provide pre-calculated values and allow you to perform advanced analytics without accessing high volumes of data.  

Similar questions the interviewer may ask:

  • When should aggregate tables be used?

  • What are some important factors to consider when deciding whether or not to use aggregate tables?

5. What is active data warehousing?

Why they’re asking this: Active data warehousing provides valuable and immediate insights for companies by identifying patterns and trends.

How to answer: Active data warehousing is the ability of a data warehouse to automate tasks and integrate real-time data. For example, when transactions occur, active data warehousing allows information to be captured immediately and produces accurate, updated information. 

Similar questions the interviewer may ask:

  • Explain the differences between an active data warehouse and a traditional data warehouse.

6. What is XMLA?

Why they’re asking this: Multidimensional data analysis sources such as data warehouses use XMLA to provide access.

How to answer: XMLA stands for XML for Analysis and is the standard for accessing data in OLAP and other data sources. XMLA uses two methods: execute and discover. Execute allows applications to execute against data sources, whereas discover retrieves information from the internet.

Similar questions the interviewer may ask:

  • What are the two accessible methods of XMLA?

  • What is a Simple Object Access Protocol?

7. What is the junk dimension?

Why they’re asking this: Utilising the junk dimension within a data warehouse increases the efficiency of queries and helps better manage stored information.

How to answer: Junk dimensions are a structure used to improve query performance within a data warehouse by providing a place to contain miscellaneous attributes. 

Similar questions the interviewer may ask:

  • What information can you store in the junk dimension?

  • Why are junk dimensions used?

8. What is a cloud data warehouse, and what are its advantages? 

Why they’re asking this: While on-premise data warehouses are still actively used, cloud data warehouses are superior for many operations. 

How to answer: Cloud data warehouses rely on the cloud to store and ingest data from various sources. Not only are they more affordable, but they are also often easier to use and manage, with strong support for both large and variable storage requirements.

Similar questions the interviewer may ask:

  • What are the differences between a cloud data warehouse and an on-premise data warehouse?

9. What are the three types of fact tables?

Why they’re asking this: Seen within star and snowflake Schema data warehouse models, fact tables store fundamental business insights and measures.

How to answer: You can use three types of fact tables: transaction fact tables, periodic snapshot fact tables, and accumulating snapshot tables. Transaction fact tables are the most basic and common, structured in a one-dimensional framework for each row. Periodic snapshot fact tables describe information at a certain point at specific intervals. Accumulating snapshot tables show information throughout an established period. 

Similar questions the interviewer may ask:

  • What is a fact entity?

  • Explain the differences between types of fact tables.

10. What is dimensional modelling?

Why they’re asking this: The interviewer wants to test your familiarity with optimal modelling methods seen in data warehouse design. 

How to answer: Dimensional modelling allows data to be retrieved faster from a database. It relies on fact and dimension tables to optimise the process and is the model typically used in OLAP systems. This design is more flexible than entity-relationship modelling and better for data analysis.

Similar questions the interviewer may ask:

  • What are the steps involved in the dimensional modelling process?

  • How does dimensional modelling differ from relational modelling?

11. Explain star vs snowflake schema.

Why they’re asking this: For data warehousing, you can choose between various multi-dimensional data models, with the star and snowflake schemas being commonly used.

How to answer: The star schema is the simplest data warehouse schema. It involves one central fact table and multiple dimension tables, making it easy to understand. Star schema can effectively query large data sets, while snowflake schemas are sometimes more difficult. Snowflake schemas also have a central fact table, but their dimension tables have sub-dimension tables that may store more complex data.

Similar questions the interviewer may ask:

  • What does it mean to ‘denormalise’ data in a star schema?

  • How many fact tables does a star schema have?

12. Why is data purging important?

Why they’re asking this: Purging excess data allows data warehouses to run faster and reduces storage costs. Different organisations will have different strategies and policies related to data purging.

How to answer: Data purging is deleting data from a database when it is no longer needed. While purging data permanently removes it from its original location, you can store it in another location as an archive if necessary. Data purging can increase efficiency and security in data management.

Similar questions the interviewer may ask:

  • What factors should you consider in your purging data strategy?

  • Can you retrieve purged data if needed? 

13. Explain the chameleon method

Why they’re asking this: The chameleon method solves problems related to current data warehousing techniques. 

How to answer: The chameleon hierarchical clustering method allows large data sets to perform optimally. It does this through a two-phase algorithm. In phase one, the algorithm clusters data items into sub-clusters. In phase two, the algorithm searches for sub-clusters to combine based on their interconnectivity and closeness. 

Similar questions the interviewer may ask:

  • Explain the two phases of the chameleon method.

  • What is a hierarchical clustering algorithm?

14. Define a data mart. 

Why they’re asking this: While data marts and warehouses store data, companies use each for different processes. 

How to answer: A data mart is a smaller subset of a data warehouse focused primarily on one specific subject. This allows a team or group to access relevant data more easily rather than taking time to search through the entire data warehouse. 

Similar questions the interviewer may ask:

  • Compare the similarities and differences between a data mart and a data warehouse.

  • Describe the structure of a data mart.

15. What are the different types of data marts?

Why they’re asking this: Depending on the relationship to the data warehouse, you can choose to use different types of data marts.

How to answer: You can use three different types of data marts: independent data marts, dependent data marts, and hybrid data marts. Independent data marts don’t have a central data warehouse; many view them as smaller data warehouses. Dependent data marts depend on inputs from the entire data warehouse and only access required data sets. Hybrid data marts combine independent and dependent data marts where external sources and an internal data warehouse contribute information. 

Similar questions the interviewer may ask:

  • How do you determine the type of data mart to use?

  • What is a subject-oriented database?

Next steps

Several highly-rated courses on data warehousing are offered on the Coursera learning platform. Consider boosting your credentials with an IBM Data Warehouse Engineer Professional Certificate, where you can build job-ready skills in database design, enterprise data warehouse, and more. 

Another option is to consider an advanced course at the University of Colorado on Data Warehousing for Business Intelligence. This course will help you gain hands-on experience working with large data sets in a data warehouse environment to create dashboards and visual analytics. 

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