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Big Data Interview Questions and Answers
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Top 20 Big Data Interview Questions and Answers

Published On: April 1, 2022

Big Data Interview Questions and Answers

Almost all businesses now employ big data technology to improve their marketing strategies and campaigns. Big Data is one of the top ten highest-paying occupations in the world for technology since it is still developing, if not more, than ever before. These are the best big data interview questions and answers to help you advance your career.

Big Data Interview Questions and Answers for Beginners

1. Define big data and explain the five Vs of big data.

A collection of complicated, unstructured, or semi-structured data sets with the potential to yield useful insights is referred to as big data. 

Big data’s five V’s are volume, variety, velocity, veracity, and value.

2. What is the volume of big data?

The volume is reflected in a sizable amount of data kept in data warehouses. The data may reach unexpected heights; therefore, it is necessary to review and handle these massive amounts of data. This could be terabytes or petabytes in size, if not more.

3. What is the velocity of big data?

Velocity describes the rate at which data is created in real-time. Consider the rate at which posts are created on Facebook, Instagram, or Twitter every second, hour, or longer  as a basic illustration of recognition.

4. Explain the variety of big data

Structured, unstructured, and semi-structured data gathered from multiple sources is referred to as big data. This diverse range of data necessitates highly specialized and distinct processing and analysis methods, along with specially designed algorithms.

5. Explain veracity.

Data veracity is essentially the quality of the studied data, or, to put it another way, it has to do with how trustworthy the data is.

6. Describe Value

Once transformed into something useful, raw data is meaningless. We can retrieve useful data. 

7. Why companies are utilizing big data to gain a competitive edge.

Regardless of the company’s size and division, data is becoming a vital tool for enterprises to use. Businesses frequently use big data to gain an advantage over competitors.

Verifying the datasets a business gathers is merely one step in the big data process. Big data specialists also need to understand how the organization intends to use the data to its advantage and what requirements it has for the application.

8. In what ways may big data support confident decision-making?

Big data continues to support the goal of analytics, which is to enhance decision-making. With so much data at their disposal, big data can help businesses make decisions more quickly while maintaining confidence in their selection. 

In today’s fast-paced world, being able to move quickly and respond to wider trends and operational changes is quite advantageous for businesses. 

9. How can asset optimization benefit from big data?

Big data indicates that companies have individual asset control. This suggests that they can increase productivity, decrease downtime that certain assets might need, and appropriately optimize assets based on the data source. 

Guaranteeing that the business is making the most of its resources and establishing connections with declining costs provides a competitive edge. 

10. How does big data contribute to cost savings?

Big data can help companies lower their expenses. Companies can identify areas where they can cut costs without negatively affecting business operations by using the data they collect, which can be used for everything from energy consumption analysis to personnel performance evaluations. 

11. In what ways does big data enhance consumer engagement?

Customers who participate in online surveys confidently provide information about their preferences, routines, and trends. This information may be utilized to craft and customize customer interaction, which may ultimately result in higher sales. 

Knowing what each customer wants from the information gathered about them allows you to target them with particular products, but it also provides a personalized touch that many modern consumers have come to expect. 

12. How does big data aid in the discovery of new sources of income?

Companies can grow into new regions and find new revenue streams with the help of analytics. 

For example, businesses can determine the best course of action by understanding client trends and decisions. 

Organizations may be able to generate additional revenue streams and form partnerships with other organizations by selling the data they gather. 

Big Data Interview Questions and Answers for Experienced Applicants

13. How are Big Data and Hadoop related?

Big Data discussions always include Hadoop as well. Thus, from the standpoint of an interview, this is one of the most important questions. that you will undoubtedly encounter. 

An open-source framework called Hadoop is used to store, process, and analyze large, messy data collections to gain understanding and knowledge. Thus, this is the relationship between Hadoop and big data.

14. Describe the role that Hadoop technology plays in big data analytics.

It takes a lot of work to analyze and handle big data because it involves a lot of organized, semi-structured, and unstructured data. A technique or tool was required to assist in processing the data quickly. 

  • Hadoop is therefore utilized due to its processing and storage capacities. 
  • Hadoop is also an open-source piece of software. It’s advantageous for business solutions if you wish to take cost into account.

The primary factor contributing to its rise in popularity in recent years is that it allows for the distributed processing of massive data sets through the use of cross-branch computer clusters that practice basic programming concepts.

15. What are the advantages of data modeling?

As a component of their data management strategy, data modeling provides businesses with numerous advantages:

  • You’ve cleansed, sorted, and modeled your data before you even start building a database, so you can project what should come next. 
  • Databases become more constrained, prone to errors and poor design, and have improved data quality as a result of data modeling.
  • Data modeling creates a visual representation of the data flow and your intended organization. 
  • This helps staff members understand data and how it fits into the larger picture of data management. 
  • Additionally, it fosters department-to-department communication about data inside a company.
  • Data modeling paves the way for more database architecture, which in turn leads to more advantageous applications and data-driven business insights.

16. What are the three operating modes Hadoop supports?

Standalone Mode or Local Mode: Hadoop is set up to run in a non-distributed mode by default. It is a single Java process that runs. 

  • This mode makes use of the local file system rather than HDFS. 
  • There is no need to configure core-site.xml, hdfs-site.xml, mapred-site.xml, masters & slaves in this mode, which is more beneficial for debugging. 
  • In Hadoop, standalone mode is typically the fastest mode.

The pseudo-distributed mode involves every daemon operating on its own Java process. Custom setup is needed for this mode ( core-site.xml, hdfs-site.xml, mapred-site.xml). 

HDFS handles the input and output. It is advantageous to use this deployment method for testing and debugging. 

Fully Distributed Mode: This is Hadoop’s production mode. Two machines in the cluster are designated as NameNode and Resource Manager, respectively; these are the masters. 

  • The remaining nodes function as data nodes and node managers, respectively, and are the slaves. 
  • Hadoop Daemon configuration parameters and environments must be defined. 
  • This mode provides fully distributed computing capacity, security, fault endurance, and scalability.

17. Which big data processing approaches are there?

Large-scale data sets are analyzed using big-data processing techniques. 

To handle arbitrary BI scenarios, offline batch data processing usually operates at full capacity and scale. On the other hand, real-time stream processing uses the most recent data slice for data profiling, safety monitoring, imposter transaction exposures, and other purposes. 

The hardest task, though, is performing quick or real-time ad hoc analytics on a large, complete data set. It essentially means that you have to quickly read through a ton of information.

Various big data processing methods include:

  • Batch Processing of Big Data
  • Big Data Stream Processing 
  • Real-Time Big Data Processing
  • Map Reduce

18. When to apply MapReduce to big data.

A parallel distributed computation model called MapReduce was developed for large data sets. A reduction function acts as a summary operation, while a map function handles filtering and sorting in a MapReduce model.

  • MapReduce is a key component of the open-source Apache Hadoop ecosystem and is widely used in the Hadoop Distributed File System (HDFS) for data selection and querying. 
  • Depending on the wide range of MapReduce algorithms available for building data selections, a variety of queries can be executed. 
  • Furthermore, MapReduce is suitable for parallel processing in iterative computations involving massive amounts of data. This is so because it depicts a data flow as opposed to a process.

The requirement to process all of the increased data we create and collect to make it usable increases with time. The programming approach for MapReduce, which is iterative and parallel processing, is useful for making sense of big data.

19. Mention Reducer’s primary techniques.

A reducer’s primary techniques are:

setup() is a method that is used only to set up various reducer settings.

reduce(): The reducer’s main function is reduced. This method’s specific purpose is to define the task that needs to be completed for each unique group of values that have a common key.

cleaning(): Following the completion of the reduce() task, cleanup is used to clear or remove any temporary files or data. 

20. Write down the command that copies information from the local system to HDFS.

To copy data from the Local system to HDFS, use the following command: 

hadoop fs –copyFromLocal [source][destination]

Conclusion

You will undoubtedly benefit from this collection of the top 20 big data interview questions and answers during your interview. Get certified at our big data training institute in Chennai to demonstrate your skills to the interviewer during your big data interview. All the best!

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