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Big Data Interview Questions

 7 Most Popular Big Data Interview Questions

     -     Nov 14th, 2017   -     Big Data   -     0 Comments

The era of big data has just begun. With more companies inclined towards big data to run their operations, the talent demand is at an all-time high. What does it mean for you? It only translates into better opportunities if you want to get employed in any of the big data positions. You can choose to become Data Analyst, Data Scientist, Database administrator, Big Data Engineer, Hadoop Big Data Engineer and so on.

In this article, we will go through the seven most popular interview questions related to Big Data. Also, this article is equally useful for anyone who is looking for Hadoop developer questions as a fresher.

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To give yourself an edge, you should be well-prepared for the big data interview. Before we start, it is important to understand that interview is a place where you and the interviewer interact only to understand each other, and not the other way around. Hence, you don’t have to hide anything, just be honest and reply to the questions with honesty. If you feel confused or need more information, feel free to ask questions to the interviewer. Always be honest with your response, and ask questions when required.

We will answer the questions that are specific. For broader questions that’s answer depends on your experience, we will share some tips and how to answer them.

7 Most Popular Big Data Interview Questions are-

1. Explain the term “Big Data” and also tell us about the Big Data Five V’s?

Answer: Big Data is a term associated with complex and large datasets. A relational database cannot handle big data, and that’s why special tools and methods are used to perform operations on a vast collection of data. Big data enables companies to understand their business better and helps them derive meaningful information from the unstructured and raw data collected on a regular basis. Big data also allows the companies to take better business decisions backed by data.

The five V’s of Big data is as follows:

  1. Volume
  2. Velocity
  3. Variety
  4. Veracity
  5. Value

Note: You can choose to explain the five V’s if you see the interviewer is interested to know more. Furthermore, if they only ask about “Big Data”, you can just choose to tell them about the big data Five V’s.

2. Tell us how big data and Hadoop are related.

Answer: Big data and Hadoop are almost synonyms terms. With the rise of big data, Hadoop, a framework that specializes in big data operations also became popular. The framework can be used by professionals to analyze big data and help businesses to make decisions.

Note: You can go further and try to explain the main components of Hadoop.

3. Do you have any Big Data experience? If so, please share it with us.

How to Approach: There is no specific answer to the question as it is a subjective question and the answer depends on your previous experience. The interviewer wants to understand your previous experience and is also trying to evaluate if you are fit for the project requirement.

So, how will you approach the question? If you have previous experience, start with your duties in your past position and slowly add details to the conversation. Tell them about your contributions that made the project successful. This question is generally, the 2nd or 3rd question asked in an interview. The later questions are based on this question, so answer it carefully. You should also take care not to go overboard with a single aspect of your previous job. Keep it simple and to the point.


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4. Do you prefer good data or good models? Why?

How to Approach: This is a tricky question as it asks you to choose between good data or good models. As a candidate, you should try to answer it from your experience. Many companies want to follow a strict process of evaluating data, means they have already selected data models. In this case, having good data can be game-changing. The other way around also works as a model is chosen based on good data.

As we already mentioned, answer it from your experience. However, don’t say that having both good data and good models is important as it is hard to have both in real life projects.

5. Will you optimize algorithms or code to make them run faster?

How to Approach: The answer to this question should always be “Yes.” Real world performance matters and it doesn’t depend on the data or model you are using in your project.

The interviewer might also be interested to know if you have had any previous experience in code or algorithm optimization. For a beginner, it obviously depends on which projects he worked on in the past. Experienced candidates can share their experience accordingly as well. However, be honest about your work, and it is fine if you haven’t optimized code in the past. Just let the interviewer know.

6. How do you approach data preparation?

How to Approach: Data preparation is one of the crucial steps in big data projects. When the interviewer asks you this question, he wants to know what steps or precautions you take during data preparation.

As you already know, data preparation is required to get necessary data which can then further be used for modeling purposes. You should convey this message to the interviewer. You should also emphasize the type of model you are going to use and reasons behind choosing that particular model. Last, but not the least, you should also discuss important data preparation terms such as transforming variables, outlier values, unstructured data, identifying gaps, and others.

7. How would you transform unstructured data into structured data?

How to Approach: Unstructured data is very common in big data. The unstructured data should be transformed into structured data to ensure proper data analysis. You can start answering the question by briefly differentiating between the two. Once done, you can now discuss the methods you use to transform one form to another. You might also share the real-world situation where you did it. If you have recently been graduated, then you can share information related to your academic projects.

By answering this question correctly, you are signaling that you understand the types of data, both structured and unstructured, and also have the practical experience to work with these.


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