Top 10 Big Data Interview Questions For Freshers!

Introduction

We are just at the beginning of the era of big data. The need for talent is greater than ever, with more businesses turning to big data to operate their businesses. What did it mean? If you choose to work in any big data role, you will have more opportunities. 97.2% of business executives say their organisations are investing in big data and AI projects. Big data technologies contain a variety of productive fields, including big data research, big data engineering, and analytics. It’s critical to know the interview questions and how to respond to them in order to succeed in getting recruited for big data roles. 

In this blog, to help you ace your next big data interview, we will explore the Top 10 Big Data Interview Questions for Freshers! and provide brief and useful answers. Infycle Technologies will help you prepare to expand your big data knowledge and advance your career! 

Top 10 Big Data Interview Questions for Freshers! 

When you attend a big data interview as a fresher, the recruiter will be asking you common questions about big data, such as “What is big data?” and “What are the 7 V’s of big data?” They ask these questions to define your understanding of big data. In order to help you be ready for and ace your upcoming big data interview, let’s go over some of the most common interview questions. 

1. What is Big Data?

Even though this question may seem simple, you should be able to respond with clarity and conciseness that demonstrate your knowledge of the term and its entire meaning. In short, you should be able to explain that Big Data is a collection of enormous data that is increasing exponentially in size and that cannot be handled, stored, or processed using standard data management tools. 

2. What are the various types of Big Data?

Big Data comes in three forms. 

Structured Data: 

This type of data implies that it can be handled, saved, and accessed in a predetermined format. It is extremely well-organized data, such as phone numbers, ZIP codes, personnel details, social security numbers, and salary details that can be quickly evaluated and saved. 

Unstructured Data: 

Data lacking any particular shape or structure is referred to as unstructured data. Audio, video, social network posts, satellite data, digital surveillance data, and other formats are among the most popular forms of unstructured data.

Semi-structured Data: 

Undefined yet important data formats that combine both structured and unstructured data. 

3. What are the Seven Vs of Big Data?

The seven big data Vs are 

Volume:

It is the amount of data that has exponentially grown over time. Some of the examples are bits, bytes, and terabytes.  

Velocity:

This is a measure of how quickly the data is expanding. 

Variety:

This is the range of data kinds that are available in different media, such as audio, video, and text. 

Value:

Value is the result of obtaining insightful information to satisfy corporate objectives and produce profit. 

Veracity:

This refers to the degree of correctness in the data analysis. In other words, it speaks to the quality of the data analysis or the degree of reliability of the data. 

Visualization:

Data presented to management for making decisions is referred to as visualisation. 

Variability:

Data that is continuously changing is referred to as a variable. 

Read on What are the 7 V’s of Big Data? to know in detail about the 7 V’s of Big Data.

4. What connection does Hadoop have with Big Data?

Hadoop is frequently brought up while discussing big data. Hence, in terms of interviews, this is one of the most important questions, which you may undoubtedly meet. For the purpose of gaining knowledge and insights, Hadoop is an open-source platform for storing, processing, and analyzing large, messy data sets. In light of this, Hadoop and Big Data are connected. 

5. In what ways may big data analysis help businesses increase revenue?

Big data analysis has grown in importance for companies. It helps companies set themselves apart from competitors and boost sales. Big data analytics uses predictive analytics to give businesses personalised recommendations and tips. Additionally, big data analytics helps companies introduce new items based on the demands and preferences of their customers. Companies are utilising big data analytics since these elements increase their revenue. Walmart, Facebook, Twitter, LinkedIn, and other well-known businesses are among those that are using big data analytics to boost their earnings. 

6. When building a Big Data platform, what are the essential measures to follow?

It is impossible to determine a single formula for deploying a big data platform. The following three fundamental steps are widely recognized as essential to implementing a big data platform: 

Data Ingestion:

This procedure involves gathering information from a variety of sources, including data logs, business applications, and social networking sites.

Data Storage:

After data extraction is finished, the Big Data volume needs to be stored in the database. For this, the Hadoop Distributed File System (HDFS) is essential. 

Data Processing:

The next step is to use specific algorithms to analyse and visualise the massive volumes of data that have been stored in HDFS or HBase. This will allow for improved data processing. Using Hadoop, Apache Spark, etc. makes this work easier to do. 

To successfully implement a Big Data model, one must take these important steps.

7. Explain and define the word “FSCK”?

Filesystem Check is referred to as FSCK. This command is used to generate a summary report for Hadoop that details the current status of HDFS. It does not fix errors—rather, it merely looks for them. You have the option to run this command on a subset of files or the entire system. 

8. What are the different input and output formats in Hadoop?

Hadoop typically uses the following input formats:

Text Input Format:

This is the standard input format in Hadoop. 

Key-Value Input Format:

Hadoop uses the Key-Value Input Format to read plain text files.

Sequence File Input Format:

This is used in Hadoop to read files in a sequential order. 

The various Hadoop output formats are as follows:

Text Output Format:

It is the standard output format in Hadoop.

Map File Output Format:

Hadoop writes the output as map files using this format.

DB Output Format:

This format is exclusive for entering output in H base and relational databases. 

Sequence File Output Format:

This format allows writing Sequence files.

Sequence File As Binary Output Format:

You can use this format to write values to sequence files in binary form.

9. When several clients attempt to write to the same HDFS file, what happens?

Multiple users cannot write to HDFS files at the same time. This is because HDFS NameNode enables restricted write; input from the second user will be refused while the first user uses the file. 

10. List the various commands for initiating and closing Hadoop Daemons.

This is one of the most crucial Big Data interview questions, which will allow the interviewer to determine how well-versed you are in commands. 

For starting every daemon, 

./sbin/start-all.sh

To end every daemon, 

./sbin/stop-all.sh

Conclusion

The field of big data is expanding at an exponential rate, which means that career prospects for big data specialists are many. We really hope that the interview questions provided above may help you succeed in the big data job. You cannot, however, undervalue the importance of the real-world experience. 

During a candidate interview, one of the most important things that companies frequently look for is practical experience. Working on actual projects before going to a job interview is therefore important. Enrolling in