Top Big Data Interview Questions (With Sample Answers)

Updated 19 March 2023

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Using big data in decision-making has become a priority for businesses, and big data professionals are in high demand. Data analysts and engineers who know how to manage and store high volumes of data usually fill these roles. If you are preparing for a big data interview, knowing some common questions that interviewers ask can help you answer them better. In this article, we discuss some frequently asked big data interview questions, provide sample answers, and share some tips on preparing for interviews.

Different Types Of Big Data Interview Questions

Knowing about the different kinds of big data interview questions can help you prepare better. Here are different ways to classify the most commonly-asked questions in big data interviews:

Basic interview questions for freshers

These are simple interview questions that assess the understanding of basic big data concepts and terminologies. Here are some examples:

Describe different types of big data.
How is Hadoop 1 different from Hadoop 2?
What is the most optimum hardware configuration for Hadoop operations?
What is unstructured data and how do you convert it into structured data?
What is data modeling, and what are its uses?
What is the meaning of commodity hardware?

Questions related to experience and skills

These questions usually enquire about past experience and skills in big data. Consider these examples to understand them better:

How do you use big data analysis to derive actionable insights?
What is the process of data preparation?
What, according to you, is the most beneficial feature of Hadoop?
What is a feature section in big data?
Describe some outlier detection methods.
Between good data and good data models, which one would you choose?

Advanced big data questions

Such questions are usually for experienced big data professionals and developers who have experience using Hadoop. Here are some examples:

What do you understand by NameNode?
Define rack awareness.
Explain different Hadoop daemons.
Define the different components of YARN.
What are the core methods of Reducer?
Define the concept of overfitting. How would you avoid it?

Hadoop big data interview questions

Hadoop is a popular big data framework, and most interviewers enquire about its usage, features, components, and other techniques. Consider these examples:

Describe the essential components of Hadoop.
What are the most common input formats used in Hadoop?
Define speculative execution in Hadoop.
Explain three modes in which Hadoop can be run.
Explain the requirement of data locality in Hadoop.
How can one skip bad records in Hadoop?

Sample Big Data Interview Questions And Answers

Here are some sample interview questions and answers for big data roles. These consist of different types of big data interview questions:

Explain the five Vs of big data

This is a fundamental big data question that interviewers may ask fresh graduates to understand how well they know the basic terms and concepts. The five Vs are commonly-used terms in big data, and you can explain them in your answer.

Example answer: "The five Vs of big data are volume, velocity, variety, veracity, and value. Volume refers to a high amount or quantity of data, velocity is the speed with which the data grows, variety describes the different formats of data, veracity is the reliability or trustworthiness, and value is turning raw data into something significant."

Describe three important business uses of big data

Businesses may use big data for various purposes, like improving efficiency, profitability, and market share. This is a basic question to assess whether the candidate knows the different ways in which a business can use big data to its advantage.

Example answer: "There are many businesses uses of big data, with the most important ones being profit maximization, asset optimization, cost reduction, customer engagement, market expansion, and accurate forecasting. When businesses make data-driven decisions they can predict outcomes more accurately, identify broader trends, and respond to changes more rapidly."

What is the process of deployment or implementation for a big data solution?

This simple big data question requires you to explain how to deploy a model or solution on a big data platform. Explain the three steps of data ingestion, data storage, and data processing to answer this question.

Example answer: "The first step would be data ingestion, which involves extracting data from different sources. This can take place periodically or be a one-time process. Next, we ensure data storage, which is to store the extracted data with HDFS or HBase, or any other storage system. The last step is to perform data processing, which is when we use a framework to process the collected and stored data."

Discuss some common data-related challenges, and how would you overcome them.

Interviewers can ask this question to both inexperienced and skilled professionals to understand how well they understand practical data challenges and what methods they might use to solve these challenges. You can answer this question from your professional experience and highlight some common challenges big data analysis generally poses.

Example answer: "Some of the most common challenges when performing big data analysis are ensuring data integrity, maintaining the privacy of users, and gathering real-time insights from data. There are many data management tools that can help maintain data quality, audit data to ensure data privacy and security, and use AI technology to keep data timely."

What is the use of Hadoop in big data?

Hadoop is a widely-used big data framework, and questions related to Hadoop are common in interviews. Recruiters and hiring managers can ask this question to freshers and experienced professionals to test their basic knowledge and skills.

Example answer: "Hadoop is an open-source framework that can help the process, interpret and store disorganized big data sets. It uses proprietary algorithms and code to analyze complex unstructured data to derive insights. Being an open-source framework in Java, Hadoop can help process large volumes of data and allows the implementation of different exploratory analyses without any sampling. Since it runs independently and supports all stages of big data analysis, we commonly use it for data collection, storage, and processing."

Explain the difference between NFS and HDFS?

A network file system, or NFS, is one of the oldest and most widely-used distributed file storage systems. Hadoop distributed file system, or HDFS, is more recent and manages big data. Interviewers usually ask this question to experienced big data professionals to evaluate their knowledge and experience. You can answer this question by highlighting the differences between these two systems on various parameters.

Example answer: "NFS is usually used to store and process small amounts of data whereas HDFS can store and process big data. Similarly, they differ in data storage as NFS is stored on a single hardware device whereas HDFS can be distributed on local drives. Additionally, NFS has no data reliability but HDFS offers the same. Finally, NFS can run on a single machine, which means there is no scope for data redundancy, which is not the case with HDFS as it runs on a cluster of machines."

What factors would you account for when using distributed cache in Hadoop MapReduce?

Hadoop MapReduce is an important layer for data processing. It helps process unstructured and structured data into HDFS and also divides data into smaller tasks to ensure the simultaneous processing of high volumes of data. Certain configuration parameters are crucial to MapReduce. Interviewers may ask big data analysts and developers this question to assess their expertise and knowledge of MapReduce. While answering this question, list as many factors as you can recall.

Example answer: "In an ideal distributed system, we consider things like heterogeneity, transparency, openness, security, scalability, concurrency, and resilience to failure."

Tips To Prepare For Big Data Interviews

The following tips can be helpful when preparing for a big-data interview:

Listen carefully. Before answering a question, let the interviewer complete their question and listen to them carefully. Big data questions can be tricky, so pause for a moment to recollect the right answer before you start answering.
Request for clarification. If you are unsure of the question or have missed any crucial detail, immediately ask for clarification. Do not hesitate to get additional information if necessary.
Revise and prepare well. Before the interview, invest time and effort in studying questions and preparing material from different sources. Revise frequently asked questions and rehearse the technical definition of important concepts and frameworks.
Carry out your resume and other documents. Customize your resume and other documents, like the cover letter, as per the job you are applying for. Also, carry all other relevant documents, like certifications to the interview.
Be punctual. Reach on or before time to prevent the anxiety or pressure of being late. If the interview is virtual, be ready at least 10 minutes before the scheduled time and test your camera, microphone, and internet connection.

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