50 Data Warehousing Interview Questions

By Indeed Editorial Team

Updated September 8, 2021 | Published February 25, 2020

Updated September 8, 2021

Published February 25, 2020

To interview effectively for a data warehousing position, it’s beneficial to review some common questions you may be asked. Careful preparation for your interview will help you to make the best possible impression. In this article, we provide the 50 most common data warehousing interview questions, as well as some sample answers.

Related: Top 6 Common Interview Questions and Answers

Jenn, an Indeed Career Coach, breaks down the intentions behind employer's questions and shares strategies for crafting strong responses.

Related: 12 Tough Interview Questions and Answers

General questions

These questions cover the basics of data warehousing. The interviewer will use these to ascertain if you have the appropriate level of knowledge for the position:

  • Define the data warehouse.

  • What is the query manager responsible for?

  • What is OLAP?

  • What is OLTP?

  • Define normalization.

  • What is data extraction?

  • What is a data mart?

  • What is a load manager?

  • What is a dimension table?

  • Define data mining.

  • What is ODS?

  • What is a VLDB?

  • Define data aggregation.

  • Define a subject-oriented data warehouse.

  • Define a mini dimension.

  • What is a data cube?

Questions about experience and background

These questions provide the interviewer with more information about how your education and experience have prepared you for the open position:

  • What is your highest level of education?

  • Did you major in computer science or information technology? If not, what was your major?

  • Why are you interested in data warehousing?

  • Are you familiar with SQL coding? How many years of experience working with SQL coding do you have?

  • How did your last position prepare you for this job?

  • Are you comfortable using word processing software?

  • What computer programming experience do you have?

  • Are you comfortable leading a team?

  • Are you able to collaborate with others who may not understand the technical nature of your job?

  • If you haven't worked in data warehousing before, are you familiar with the terminology?

  • What is the largest data set you have handled up to this point?

  • If you were a data warehouse person in a different industry, do you feel your skills will translate to this industry?

  • Would you be willing to take continuing education courses if requested?

Related: How To Introduce Yourself in an Interview

In-depth questions

These questions require problem-solving and making connections. Your interviewer will use them to determine if you are capable of addressing challenges:

  • List five ways to apply data warehousing.

  • How do data warehouses and operational databases differ?

  • What costs are included in data mining?

  • Compare and contrast the uses of a star schema versus a snowflake schema.

  • List the steps to build a data warehouse

  • What do you think the most important responsibilities of a data warehouse manager are?

  • What are the steps to build a data warehouse?

  • Compare and contrast a database and a data warehouse.

  • What is the difference between the Kimball philosophy of data warehousing and the Inmon philosophy of data warehousing?

  • Explain the difference between FastExport and BTEQ Export.

  • How do data analytics apply to data warehousing?

  • Explain the difference between OLAP and OLTP.

  • How many types of OLAP servers are there? What are the differences between them?

  • Give three applications of a data warehouse.

  • What are the phases included in the process of data warehouse delivery?

Related: 10 Questions To Ask After an Interview

Interview questions with sample answers

We have provided a few questions and sample answers to help you prepare for your data warehousing interview.

What is business intelligence?

Business intelligence, also known as Decision Support System or DSS, is how the data is collected, integrated and analyzed. It is helpful to know the content of the data to properly catalog it.

Example: "Business intelligence is an important aspect of data warehousing. It's also called a Decision Support System or DSS. DSS helps the data warehouse person properly collect business data to analyze or integrate the information as needed."

Describe one difference between a data warehouse and an operational database

A data warehouse is simply a collection of an organization's data. It can be used for analysis, but it has been cataloged and archived until needed. An operational database contains data that is currently in use by the organization.

Example: "The key difference between a data warehouse and an operational database is how the stored data is used. Data warehouses have data cataloged and waiting for future use. Operational databases contain data in daily use by the company or organization."

What is a dimension of data warehousing? What is the function of a dimension?

A dimension of a data warehouse is a classification or catalog. Data can be stored within these dimensions for ease of locating and analyzing that data at a later date.

Example: "Dimensions allow for systematic storage of data within a data warehouse. For example, all client information could be stored under the dimension "clients." This will make finding that data easier in the future."

Explain the different types of data marts

A data mart is an aspect of a data warehouse. Data marts are usually built for specific departments to store their data. The three types of data marts of dependent, independent and hybrid.

Dependent data marts pull data from existing data warehouses. Independent data marts pull data from external sources. Hybrid data warehouses pull information from both existing data warehouses and external sources.

Example: "There are three types of data marts: dependent, independent and hybrid. Dependent data marts provide centralization for your data, as they pull information from existing data warehouses. Independent data marts are good for organizations that need more data divisions. The information is pulled from external sources. Hybrid data marts allow an organization to access both existing data warehouses and external sources for their data."

Our company relies heavily on spreadsheets for our data. Should we use multidimensional OLAP or relational OLAP?

Multidimensional OLAP stores data in a multidimensional cube that is compatible with popular spreadsheeting programs. Relational OLAP stores data in a relational database that is accessed by the structured query language.

Example: "Between the two OLAP storage options, multidimensional is the one that provides easy communication with spreadsheeting applications. I would recommend using MOLAP over ROLAP."

What are a few benefits of a data warehouse?

Data warehouses offer many benefits. Data warehouses create easy access to an organization's total data catalog. Data warehouses save money by storing all the data in one location rather than spread out. Organizational decisions can be made more quickly and easily with easy-to-access data. Data warehouses save time with clear data dimensions for easy searching.

Example: "A few of the top benefits of data warehousing include saved time, easier decision making and lower costs. If a data warehouse has clear and consistent labeling procedures for its dimensions, data will be easy to find and analyze, which leads to easier decision making. A single, well-organized data warehouse saves the organization money by keeping all data in a central location."

Jobs in data warehousing

Knowledge of data warehousing can be useful in many jobs that manage large amounts of data or help store it securely. Here are 10 jobs in data warehousing to consider:

1. Data engineer

2. Database developer

3. Data warehouse analyst

4. Data strategist

5. Cloud database administrator

6. Database tester

7. Data modeler

8. Digital marketing manager

9. Data consultant

10. Medical records specialist

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