Big Data Interview Questions They Might Ask You

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If you have an interview for a Big Data opening and you are wondering what big data interview questions they will put you through, this article will help ease your stress. So, it will give you a broad idea of what to expect during the big data interview so that you can go fully prepared.

Probable Big Data Questions Candidates Can Expect

1. What is Big Data and the 3Vs associated with the term?

Big data refers to large and complex datasets that require advanced tools for accurate analysis and processing. Additionally, the three dimensions of big data are volume, variety, and velocity. The volume describes the size of the data. Variety means various types of data generated from different sources. Velocity refers to the pace or speed of data generation.

2. Tell me about some of the challenges of big data.

The huge volumes of data generated by the minute may be too massive for regular processing or to be managed by traditional applications and tools. The variety and velocity of data generated also make it challenging for data managers and specialists to handle the data effectively using existing and available tools.

3. How can big data be used in various business situations?

Big data can be used to improve the decision-making process in any business. This big data interview question can help managers make accurate decisions and improve several critical aspects of business, such as customer care, services, marketing, shipping, product designing, pricing, and others. In major organizations, big data can help improve the outcome of major decisions that involve applying expertise and experience.

4. What do you understand by the term Big Data Analytics?

Big Data analytics is the process of analyzing huge volumes of data generated from multiple sources. So, the data may surge from social networks, videos, digital images, sales records, reports, research papers, etc. Likewise, the purpose of analytics is to discover the patterns in the data and establish connections that point to a specific behavior among users. Moreover, by decoding and using the insights, businesses can make more accurate analyses and arrive at decisions that boost business prospects.

5. Why do you think big data analytics is important?

The most significant advantage of Big Data analysis is the ability it provides organizations to harness data accurately and generate new business opportunities. So, by applying analytics to big data, businesses can make powerful business moves to help them race ahead of others and establish market dominance. Also, they can make business operations more efficient at several levels, make customers happy and enjoy greater sales and profits.

6. What is Data Cleansing?

Data cleansing or scrubbing includes the process used to remove corrupted, duplicated, and inaccurate data. Data cleansing can help improve the overall data quality by eliminating unwanted elements from the data.

7. Mention some of the most probable sources of unstructured data.

Big data consists of both structured and unstructured data. The latter means a raw form of data that needs structured and corrected before used for your business improvement needs. Some of the most prominent sources of unstructured data are:

  • Documents and text files
  • Websites and servers
  • Sensor data
  • Also, Audio and video files and digital images
  • Social media content
  • Email communications

8. Can you provide a quick view of clustering?

Clustering is the process of grouping several similar objects in a dataset. You can create several clusters of different objects, so they become convenient to handle during data processing. Clustering is a crucial element in the data mining process. It is also used in the statistical analysis of data. Some of the methods of data clustering are partitioning density-based separation and hierarchical separation.

9. Can you tell the critical differences between Data Mining and Data Analysis?

In data mining, a hypothesis is not needed. You need well-documented and clean data. Likewise, data mining results are complex and cannot be easily understood. So, these algorithms develop an equation automatically.

Data analysis is based on a hypothesis and involves data cleaning. So, start with any data, even ones not well documented. Then, analysts must develop data analysis equations.

10. What is the K-mean Algorithm?

In big data, the K-mean algorithm refers to a partitioning technique. So, in this method, objects are categorized into K groups. Likewise, the clusters are sphere-shaped, and the data points are allied around that cluster. In this algorithm, the cluster variance is similar to one another.

11. List the most commonly used analytical technique categories

In today’s business world, the most common analytical techniques are:

  • Forecasting
  • Regression analysis
  • Additionally, machine learning (ML)
  • Data mining
  • Statistical methods
  • Database warehouse
  • Also, Database querying
  • Machine learning and data mining


These include some of the most commonly asked big data interview questions by interviewers while shortlisting big data candidates. Try to add more questions to the list and find answers to them. The more you learn about big data, the better the chances of cracking the interview.