Back
Decor-dark

15+ Best Data Warehouse Interview Questions To Get Hired With

3322 words Read time 16:25

If you are looking for data warehouse interview questions and answers, look no further. I’ve got the best way to prepare for any upcoming technical interview that you might have where the company you are applying for has a data warehouse. Before we get into the interview questions, I want to provide you with some knowledge on what and why a Company might have a data warehouse for. Its important that we start from the beginning of the equation, so that you know why the company values this asset. This will ultimately help you speak to the manager who is interviewing you with more clarity and be able to hold yourself in a confident position while discussing the data warehouse as a whole.

data warehouse interview questions

Lets go through the basics. A data warehouse is most commonly used by technology organizations who are dealing with heavy data analysis. For example, lets think about Google for a moment. In Google’s scenario, they are constantly indexing a large number of Web sites, inside of those Web sites is a large amount of information which they ingest and analyze. They perform a number of complex functions on the data itself in order to understand it more clearly, which ultimately comes back out to you. In this example, as I’m sure you could imagine, I’m talking about the core search engine behind Google. Now in Google’s case, they would absolutely use a data warehouse. Why you might ask? Well because having all of the data combined with the database information used to keep track of and/or service a single users instance of the application seems like a heavy workload and incredibly inefficient. The data needs to be stored separately, analyzed separately and then accessed by the core functioning application. This in theory and in practice is the benefit of having a data warehouse as part of the technology stack.

Increasingly, we are seeing data warehouses being used. That’s because the amount of data and the types of functions being performed by applications and computers is increasing. We are starting to trust them with our financial information, which is expansive. As well as many other types of jobs which are computationally intensive without heavy amounts of data analysis. Thus the needs of the sources of storage has also increased with our reliance on these technologies.

Before we jump into the interview questions, you may want to start with this helpful youtube clip about data warehouses. In my opinion its helpful to hear how others describe them, so that way when an interviewer asks you a question related to the topic you’ll have something to reference in terms of explanation and delivery. I hope the video below is helpful for you before we get into the questions and mock answers.



Data Warehouse Interview Questions & Answers Table of Contents

1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.

1. Can you tell me what a data warehouse is?

Absolutely. A data warehouse is a database. But it is a special kind of database that is separate from the database that we use for everyday applications and everyday functions of our software. A data warehouse is usually used as storage or large amounts of data sets. These data sets can be organized, unorganized, classified or not classified. Because of this random nature to the data inside the data warehouse and the potential large size to the overall database, it becomes quarantined from our production database. Data inside the data warehouse can be analyzed, organized, accumulated and/or archived. It is the store of all data related to future analysis and decision making for our functional products and/or services.

2. Can you tell me why a company might use a data warehouse?

A company might use a data warehouse if they require the storage of large data sets. This could be from consumer applications, which require the consumption of consumer data in a large manner in order to get any pattern recognition or signals from the large amounts of data to business applications which need to store data for their clients. A business usually uses either one or many data warehouses to find protection as well as the ability to analyze and determine the best paths forward from the data that sits inside the warehouse.

3. Why would a company use a data warehouse for data?

Combining data that was ingested, collected or imported into the core database which powers either business or user functionality creates incredible risk. Risk of failure of the core features or services which users or customers may depend on. A company would use the data warehouse for this type of data so that it can remove those potential risks yet still collect, analyze and determine what they’d like to do from larger data sets with significant ease.

4. What are the common methods a data warehouse is analyzed?

There are a few ways that a data warehouse is analyzed. First, the analysis may happen on a manual level. That means an administrator inspecting the directories or levels of which the data is stored and performing some type of manual analysis on top of that set of information. The second would be the programmatic analysis of the data. This means that the engineering team has developed some type of way of sorting through the information inside of either a single or many data warehouses to be able to determine the common patterns for which their applications need to move forward. In that case, the data warehouse is being used as an ingestion system with the analysis being put on top it programmatically, to power some type of software.

5. What is a data warehouse commonly built on?

A data warehouse is going to be most commonly built on large scale databases. That means either NoDB infrastructures like Mongo or scalable table based systems like MySQL.

6. What is data warehouse management?

Data warehouse management refers to the software and process for which data is commonly analyzed and determined for next steps. These next steps sometimes include the archiving or removal of the data inside the warehouse. Data warehouse management is sometimes a single piece of functionality that is programmatically built for the data warehouse itself. When these types of cleanup functions happen on a persistent basis, the engineering department of a company will try to build solutions for which the data management can happen more frequently, without human intervention. In more unique scenarios, where the data must undergo a human eye, the data warehouse management software usually lets the administrators sort through what they need to and determine what they’d like to do with the data at that point.

7. How does the data warehouse communicate to the core application or core database?

When two applications need to speak with each other and provide data either back and forth or from one to the other, the applications are often connected by secure connection and authentication of a single server IP address and another single server IP address. In some cases, API endpoints may be built on the data warehouse via small applications that allow another application to access the data stored within the warehouse. This is also not uncommon. Though, in scenarios like that one, you may expect that application to also be performing the cleanup/tidy/analyze type of functionality as well before it is ready to be ingested by the production application during the point of which its being called.

8. How do you treat data which needs to be archived?

Archiving data within data warehouses is usually a tricky scenario. Often times owners of these data warehouses never truly want to delete the data because of the steps that may have been taken to ensure they get the data in the first place. So what they may do is actually move any archived data to a particular cluster of the database, one that no longer has any analyzing power going towards it. That way, if at any point, systems need to be restored or data needs to be analyzed in a larger setting, the archived clusters of data can be unpacked, set back into the data warehouse and a much larger index and analysis can occur, allowing whatever problematic that the engineering or business teams are facing to be potentially alleviated.

9. How do you handle data which needs to be backed up?

Backing up of data for data warehouses is another very tricky problem, similar to the archiving functionality. Usually, a data warehouse can be set on a distributed number of servers. Because of this, it is possible to ensure that every server is sharing in the backup snapshots of the large data sets. Though, this becomes more difficult as the data gets larger. In those scenarios, master and slave server storage clusters may be the appropriate way to move forward. In that instance, every server then has a particular cluster for which is part of a larger master snapshot and each cluster is consistently backed up to its slave server. This allows a particular part of the cluster to be backed up as well as unpacked in the instance where an engineering event requires the backed up snapshot. The way to think about this is like a puzzle, each piece playing a role to the broader picture.

10. Can you explain to me what ‘cold storage’ is?

Cold Storage is most commonly used for data warehouse which contain information that is extremely secure and that needs the ability to be backed up for certain. Even in the event of a well architected system of smaller data warehouses that feed to the master snapshot, there’s chance of catastrophic failure. Cold Storage is when there are recurring events when the snapshot is stored onto a physical drive or other type of storage device and then placed into a physical and secure vault. These devices are then collected and stored over time to serve as the ‘cold storage’ device. Cold Storage simply refers to the fact that the storage of information is not connected to the internet and that the place of the storage is in a completely secure situation, like a bank vault.

11. What are some of the cons to having a data warehouse?

The biggest cons to having a data warehouse is the fact that is needs to be managed on a regular basis. The large data sets that engineers have to deal with can be time consuming to handle. Mostly because of the computational power that may be required to query or execute on local development machines. The amount of time that an engineering team can spend on this can be detrimental to the progress that the team could be making building more features and facilitating the requests of customers.

12. What types of programming languages do you need to know to work within a data warehouse?

You really should know SQL if you are going to be dealing with data warehouses. Any type of database programming language is a must. But SQL is the most common and the most powerful.

13. What is a production database?

A production database is the core database that is used to power the products and services that may be more customer facing. It should be lightweight and ‘close touch’ with the things that support our value proposition as a company or service of software. Some people would call this an operational database, this refers to the fact that this database provides all of the core operations that are part of the main value proposition and/or product/service.

14. How would you secure a data warehouse?

Data warehouses need be secured by SSH keys, encrypted passwords and potentially VPN networks if that’s a must. This means that only certain locations and IP addresses are able to attempt to login to the master machine. This is usually the best way to secure a data warehouse, though there are many other ways that are less common as well.

15. How would you describe a data warehouse to a non-technical colleague?

In order to explain a data warehouse to someone who isn’t technical, I would probably try to help them understand via a fantasy or imaginary scenario. I would tell them to picture many hundreds of people standing in a field. All of these people have various colored tee shirts on. They are also all mixed together, so that there’s no clear grouping of the people, their tee shirts, or any other pattern. And that the problematic attempting to be solved was to group the people in the field together by their tee shirt color. The way you would do that is by first trying to take a chunk of the people who are standing in the field and telling them to stand together but by the side, you then may break the group of people up into smaller chunks, maybe of four. Then, through those smaller groups you would ask the people who have the same color tee shirt to start grouping themselves together. You would do this because you’d be able to see that group of people more clearly and could provide them better direction. Then, you might do that with the initial groups of people you set aside. Finally, you may have sets of people who now all have their tee shirt colors coordinated but need to be merged together into one final grouping. This would seem like a very logical and organized way to deal with such a large number of people, correct? If you can picture this scenario unfolding, then are picturing the need for a data warehouse and the analysis that must be performed on top of it to make for successful computational events.

16. Can you tell me what metadata is?

The best way to describe what meta data is as information regarding data. For example, if we had an MP3 file, inside of the meta data may be information regarding the producing artist as well as the company which produced the song or album.

17. What are data relationships?

Data relationships is when groupings of information are then labeled as similar. This allows data to be clustered, analyzed, labeled and then propelled into the potential operational database. Relationships are nearly exactly how they sound, similarities or patterns between the information that may be within the data warehouse.

18. What are single and multi source data warehouses called?

Generally speaking, there are two types of abbreviations which help to describe how many sources and what types of functionality can be done within a data warehouse that either has a single or multi-ingest type of core functionality to it. This is OLTP and OLAP. The differences between these two things is that OLTP is when there is a single source for the data ingestion, which usually defines a very strict usage of that data. And then OLAP means many sources may be used to ingest and/or collect data, where the functions that can be performed on that data set is much more vast.

Conclusion and preparation

I hope this guide was helpful for you going into your interview. It's important to recognize that most of your knowledge with regard to regular programming and database development as well as management, will take you very far in the process. Generally speaking, most data warehouses are simply large groupings of databases which are connected together and managed in a more unique fashion than a production/operational database. As long as you feel confident about your abilities in this field, which is SQL and database programming, then you should be absolutely fine in your upcoming interview. Be sure that you spend the time to go through these questions and answer them in your own way, you’ll need to be sure that you are delivering the answers to these questions in a confident, secure and concise manner. The only way that you can do that is simply by practicing before the interview.

If you have any other questions that you’d like to see part of this guide, by all means please email me. Also know that all of my questions above are simply mock questions.

Share this article

C

Other articles