Big Data Interview Questions and Answers

Last update: 16 May 2020, 11 Questions

Big data is actually a field that finds ways to analyze and extract information systematically through data sets which are very large or intricate to be dealt with by conventional data processing application software. If you want to read more about Big Data interview questions for a job position in a similar field, feel free to scroll down and have a read.

In general, data sets with many cases or rows offer a much greater statistical power, meanwhile, the data presenting a higher probability of challenges i.e. more columns may lead to a higher false discovery rate. The most common Big Data solutions include capturing of data, data storing, data analytics, and much more.

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Most Frequently Asked Big Data Interview Questions

Here in this article, we will be listing frequently asked Big Data Interview Questions and Answers with the belief that they will be helpful for you to gain higher marks. Also, to let you know that this article has been written under the guidance of industry professionals and covered all the current competencies.

1. Why is big data important for organizations?

Big data analytics is a comparatively new technology helping organizations to harness their own data and optimize its use for identifying new opportunities. Here are some of the ways Big Data is vital to organizations:

  • Cost reduction: It uses technologies like cloud-based analytics and Hadoop which effectively bring down costs a lot, especially when storing large amounts of data. In addition to that, analytics helps identify multiple efficient ways to increase productivity.
  • Faster and better decision making: Combined with the speed of Hadoop and in-built memory analytics, along with the capacity to analyze new sources of data, organizations are able to analyze vast amounts of data instantly and make decisions based on them.
  • Launching new products and/or services: Combing through large amounts of data gives the organizations the power to serve their customers on a superior scale while satisfying their needs instantly. This leads to the launch of new products and/or services to help grow and retain their existing customer base.
2. What are the five V’s of Big Data?

Here are the five V’s of Big Data and how they help organizations to scale their business:

  • Volume: Sheer volume of data is one of the first features of Big Data helping businesses in making better and informed decisions. Velocity: Sometimes, Volume can be beaten by Velocity or speed of acquisition of data. This is vital as companies face cut-throat competition and speed can be a big factor in gaining an upper hand here.
  • Variety: Big Data has a major advantage in obtaining data having a lot of variety. This can help companies in the service industry where variety is considered a very important feature of gaining superiority among competitors.
  • Veracity: Volume and Velocity are good only when the quality of data is good, ain’t that true? Big Data comes to the rescue here by providing quality data to help in accurate decision making.
  • Value: This is the most vital aspect. You have large amounts of data that are acquired at a very high speed. But, you need to know whether this is good enough or not. Big Data provides you with more than just data. It helps you analyze it by bringing value to the table.
3. What is the distributed cache and what are its benefits?

Distributed caching is a popular method for caching storage data which has been configured across various nodes and servers in the same network. Caching the data which has been stored in similar data request pieces of information.

Benefits of Distributed Caching Method:

  • Reduced Network Costs
  • Enhanced Responsiveness
  • Optimized performance on the same hardware settings
  • Round-the-clock availability of content even during network interruptions.
4. Why do we need Hadoop for Big Data Analytics?

Here are the reasons for using Hadoop in Data Science:

  • Engaging Data with Large Datasets
  • Simplified methods of Data Processing
  • Using its flexible schema for Data Agility
  • Providing linear scalable storage for Data Mining
5. What is Fsck?

FSCK is an admin command in Hadoop which is used to check the HDSF File System to enable the passing of different results with different arguments during Data Analytics.

6. What are the steps involved in big data solutions?

Here are the 6 steps involved in setting up any Big Data Solution

  • Analyzing the Business problem to be solved
  • Vendor Selection for Hadoop Distribution
  • Selecting a Deployment Strategy, i.e. On-site, cloud-based or both
  • Overall Capacity Planning
  • Final Infrasturce Sizing
  • A Backup and Disaster Recovery Plan
7. What is the purpose of the JPS command?

JPS(Java Virtual Machine Process Status Tool) is a command which is used to display all java based processes for a particular user in Hadoop. It is also used to check all the Hadoop Daemons like Data Node, Name Node, Resource Manager and more running on the machine.

8. What are the tools used in big data processing?

Here are the 10 most useful tools used in Big Data Solutions

  • Hadoop
  • Apache Spark
  • Apache Storm
  • Cassandra
  • Rapid Miner
  • MongoDB
  • R Programming Tool
  • Neo4j
  • Apache SAMOA
  • HPCC
9. What is the difference between big data and data science?
Big Data Data Science
Used to handle large amounts of data Used to analyze the data
Used for processing large amounts of data while generating insights Used to understand a pattern in the data sets which help in decision making.
Identified by volume, veracity, variety and velocity of data Identified by the processing of Big Data and the solutions it brings to the table.
Includes structured, semi-structured and unstructured data. Includes forecasting, decision-making prediction and classification based on the data.
Generally used by the Ecommerce, Telecommunication and Security Industries. Generally used for Sales, Image Recognition, Risk Analytics and Digital Advertisements
Tools used are: Spark, Hadoop and Flink Tools used are: SAS, Python and R
11. What are the steps to deploy a big data solution ?

Here are the 4 steps to successfully deploy a working Big Data Solution:

  • Finding a quality source of Data as this is where the first step of any Big Data Solution starts.
  • Integration of the Data Sources and a method for storing the data.
  • After the integration and storage of data, analyzing the data is important through data models and analytics tools.
  • Finally, after analyzing the data, setting up a platform for Data Visualization and Reporting for quick decision making.

The current usage of the term Big Data almost all the time refer to the usage of predictive analytics, enhanced user behavior analytics, or even some other superior data analytics methodologies which are used in extracting values from data sets, and rarely to a particular size of data set.

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