Top 25 Big Data Interview Questions and Answers in 2024

Big data has entrenched the IT job market. Several companies are constantly looking for specialists, creating room for over twenty different types of big data positions. Most IT specialists are also turning to big data now that it has proven to be a lucrative field.

Therefore, you should prepare well for an interview if you have applied for a big data job to stand a chance. This article looks at some of the frequently asked questions during such interviews, complete with their answers, to give you an idea of what to expect.

Let’s take a look.

1.    Why Are You Interested in This Role?

I am passionate about working with startups. Given that your company is still young, I would like to apply my years of experience to help you manage data and have a formidable IT department. Your company has also shown great growth prospects, and I would like to be part of the team that drives it into an IT powerhouse.

2.    What Are the Roles of a Big Data Specialist?

A big data specialist collects, stores, manages, and protects data through advanced computerized models. He/ she also ensures that all the data privacy laws are implemented, and all data protection regulations are upheld.

3.    What Are the Qualities That This Position Requires for You to Be Effective?

This job requires someone who has excellent analytical, data visualization, and problem-solving skills. He/ she should also be a good programmer and data miner. Anyone involved in big data must be familiar with technologies, business domains, and big data tools. Lastly, one must be a critical thinker and possess good communication and presentation skills.

4.     What Major Challenge Did You Face During Your Last Role? How Did You Overcome It?

I prefer working with teams. However, during my last role, the hiring firm preferred individual work over teamwork, which posed a considerable challenge. I met with the human resource manager and convinced him about the importance of having a team while working on data and machine projects. He understood, consulted with the top management, and was allowed to put up a team. We finished the work in record time, and everyone was happy.

5.    Describe Your Daily Routine as a Big Data Specialist?

My daily routine as a data scientist starts with meeting with my team, where we discuss ongoing projects and how we can overcome the associated challenges. I then check my emails and respond to the most pressing ones before working on my current projects. I help my team members work on their different models in the afternoon before ending my day at 5 pm to catch up with some tech news. 

6.    Describe Briefly About Your Big Data Experience?

I have worked in the IT and data industry for over fifteen years. I have first-hand witnessed different technological improvements and inventions. I have spent years in research and development, working for various organizations. I have developed and tested different algorithms and even written published mathematical proofs, which I have attached to my CV, to simplify given data problems.

7.    What Kind of Strategies and Mindset Is Required for This Role?

Just like the name suggests, big data is enormous and therefore impossible to handle as an individual. I prefer working with a team when undertaking projects, however small they seem, a strategy that saves time and improves results. I also keep an open mindset when working on different projects, which helps me accommodate other viable suggestions and new developments.

8.    What Is the Biggest Challenge That You Foresee in This Job?

I have to commend your IT team for the fantastic job they have done for the company. Your firm has already solved the main challenges that I faced in my previous working places, such as lack of equipment and outdated tech. It isn’t easy to point at a specific challenge from the outside. With the IT team present in this firm, I believe that we can handle any future challenges.

9.    How Do You Stay Motivated at Work?

I spend part of my time building relationships across departments at my company, which helps me enjoy my work environment. Meeting different people with different problems and coming up with solutions based on my big data skills and experience keeps me going. I also take some time to rest and catch up with tech news which cools me off and lights me up to continue with my projects.

10. Describe a Time When You Failed in This Role and The Lesson You Learned?

 Working in the IT department can be pretty challenging due to the strict deadlines and their importance to the organization. I failed to handle a project on time during my first year as a data specialist, which shut down some of the company’s operations for a day. This taught me the importance of working as a team and asking for help if work proves overwhelming.

11. Define Big Data and Explain Its VS

Big data is a collection of complex data sets that one can use to arrive at actionable insights. These can either be structured or semi-structured. It has four VS, namely volume (amount of data), variety (various data formats), velocity (increasing growth speed), and veracity (available degree of accuracy).

12. Tell Us About the Core Methods of a Reducer

The three core methods of a reducer are setup (), reduce (), and cleanup (). The first is used to configure parameters such as the heap size, distributed cache, and input data. The second reduces an array to a single value, and the third clears all the temporary files.

13. How Can Big Data Add Value to Our Business?

Owning data gives you power. Your business can use analytics from big data to convert raw data into meaningful insights that can help you develop or improve your business strategies. You can also use big data for proper decision-making since you will be basing your decisions on tangible information and insights instead of empty predictions.

Predictive analytics from big data will also help your company develop customized recommendations and marketing strategies to appeal to different buyers.

14. How Does One Deploy Big Data Solution?

A big data solution is deployed in three steps. The first is data ingestion, which entails collecting data from different sources and extracting it through real-time streaming or batches. The second is data storage, where the data is kept in a database after extraction. The last step is data processing, done through different frameworks such as Hadoop, Spark, and Flink.

15. What Do You Understand by a Distributed Cache? What Are Some of The Benefits?

 Distributed cache is a MapReduce framework’s service used for caching files. Hadoop ensures that the file exists on individual DataNodes when a file is cached for a specific job. Therefore, you can access and read the cached file if you want to populate any collection in a code. (You can include benefits such as tracking the modification timestamps of cache files and distributing simple, read-only data files).

16. What Is the Role of a JobTracker?

The JobTracker manages the TastTrackers, thus playing an important role in resource management. It tracks resource availability and conducts task life cycle management. It monitors the execution of the MapReduce workloads and each TaskTracker before submitting the overall job report to the client. Also, based on the available slots, the JobTracker allocates TaskTracker nodes.

17. Tell Us How You Handle Missing Values in Big Data?

I acknowledge that a data specialist must handle missing values properly to avoid erroneous data, which generates wrong outcomes. I, therefore, treat such values correctly before processing the datasets. If the number of missing values is small, I drop it, but if there happens to be a bulk of missing values, I resort to data imputation. I may also engage statistics to estimate the missing values through regression, multiple data imputation, pairwise deletion, or approximate Bayesian bootstrap.

18. What Is the Relationship Between Hadoop and Big Data?

Hadoop is an open-source framework that stores, processes, and analyzes complex unstructured data sets used to develop insights and intelligence. It is what stores, processes, and analyzes big data.

19. What are HDFS and YARN? What Are Some of Their Components?

The HDFS is the default storage unit of Hadoop, which stores different data in distributed environments. It has two components, namely NameNode and DataNode. NameNode is the master node and contains the metadata information for all the data blocks. The DataNode includes slave nodes responsible for data storage.

YARN, short form for Yet Another Resource Negotiator, manages resources and gives them an execution environment. Its two main components are the ResourceManager and the Node Manager, which execute tasks on all DataNodes.

20. What Do You Understand by Commodity Hardware?

Commodity hardware is the minimal hardware resources that a system requires to run the Apache Hadoop Framework. Therefore, any hardware that supports Hadoop’s minimum requirement can be referred to as commodity hardware.

21. How Will You Ensure That Hadoop Is Secure?

Hadoop security is achieved through Kerberos, which has three steps that one must adhere to for high-level access. The first is the client’s authentication to the authentication server, which produces a time-stamped TGT. The second is authorization, where the client uses the TGT to request a service ticket. The last is a service request where the client uses the service ticket to authenticate to the server.

22. What Are the Different Approaches to Dealing with Big Data and How Does One Arrive At Them?

The approach arrived at dealing with big data depends on the business requirements and the budgetary provisions. An organization must first decide on the business concerns and the questions it intends the data to answer. This will help arrive at the two approaches: batch processing and stream processing.

Depending on the business requirements, an organization may process the big data in batches daily or after a given duration. Stream processing happens every hour or even fifteen seconds as per the business demands. All in all, the right approach depends on the business objectives and strategies.

23. What Are the Different Platforms to Deal with Big Data?

The big data platforms are either open-source or license-based. The most powerful big data platform in open-source platforms is Hadoop. The other alternative is HPCC, fully written as High-Performance Computing Cluster.

For license-based platforms, we have Cloudera (CDH), MapR(MDP), and Hortonworks(HDP), among many others. In case a business requires stream processing, a platform such as Storm comes in handy. This landscape is better understood if we consider the intended usage of big data.

24. Explain the Link Between Business Revenue and Big Data?

Big data provides insights that help in inventory management, production, marketing, and service offerings, directly influencing business revenue. It further helps an organization increase its efficiency at every stage of the business, cutting down on expenses and driving in more profits, which allows it to remain competitive.

Big data analysis also offers insights on market demands and customer preferences, which can boost government revenue when used well. One can also use the recovered insights to formulate business strategies, increase sales conversions which ultimately generates more revenue.

Big data allows an organization to consolidate data from different departments and several sources to answer business concerns, reducing inventory management costs.

25. What Are Some of The Factors That Should Be Considered When Building Big Data Models?

Big data is less predictable compared to traditional data, and its creation process must, therefore, be given special emphasis. Its complexity poses the need to reorganize and rearrange the business data as guided by the business processes.

Therefore, data models should be crafted to have logical inter-relationships among different business data. The data interfaces should also be elastic and open to accommodate the unpredictable nature of big data.

The specialist should use only the data applicable to the business in building the models.


These are some of the frequently asked questions in big data interviews. I hope that our answers have given you a clear guideline on how to approach them. We wish you all the best in your interview!

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