Top Big Data Interview Questions and Answers for Job Seekers

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by Adilin Béatrice


May 31, 2021

Analytics Insight has listed the 10 most asked big data interview questions and their appropriate answers.

Big Data is a revolutionary concept that has changed the tailwind of business improvement. Technology has changed the way organizations collect and analyze data, and it is expected to continue to evolve in the future as well. The careful analysis and synthesis of big data can provide valuable information to help them make informed decisions. As big data and big data analytics become buzzwords in the tech sphere and beyond, the demand for skilled professionals to fill big data jobs is also increasing. A growing number of companies are looking for talented candidates with skills relevant to making sense of the large data sets they deal with. However, deciphering a Big Data interview is not easy. Aspirants should be familiar with many aspects of technology and be familiar with many tools and their features. Even seasoned professionals are often faced with big data questions, which they stutter to answer. Therefore, Analytics Insight has listed the 10 most asked questions in big data interviews and their appropriate answers.

Top 10 Most Asked Big Data Interview Questions and Their Answers

How to define Big Data?

Reply: Big data is complex and large data sets. Because the relational database is unable to handle such dense data, companies use special tools and methods to perform operations on it. Around the world, more and more organizations are realizing the importance of Big Data and using it to meet the needs of their customers. They help companies better understand their business and help them gain meaningful insights from raw, unstructured data collected on a regular basis. Big data is what drives business decisions for many companies.

What are the five Vs of big data, explain them?

Reply: The five Vs of Big Data stand for volume, speed, variety, truthfulness, and value. Their definitions are as follows,

  • Volume-Volume speaks of the amount of data stored in a certain place or in a form. It is represented in petabytes.
  • Velocity – Velocity represents the ever-increasing discourse in which data grows. For example, social media is a major accelerator of big data.
  • Variety – Variety defines the different types of data that an entity receives. Data comes in different forms such as text, audio, video, images, etc.
  • Truthfulness – Truthfulness refers to the degree of accuracy of the data available. Most of the time, it represents the uncertainty of the available data raised due to its high volume which brings incompleteness and inconsistency.
  • Value- Value indicates how an organization turns its big data into value. For example, a business obtains information about decision making from big data.
What type of added value does big data offer a company?

Reply: Big Data is like a magic tool that could completely transform a business. It contains everything including the patterns, trends, and information needed to transform a business. The trick, however, is that the content is hidden in Big Data and needs professionals to uncover it. By making good use of big data, organizations can shape their current and future strategies, reduce unnecessary expenses and increase efficiency. Big data also offers leniency to companies to understand the market in general and their customers in particular in a very personalized way. This helps companies to provide personalized solutions based on the needs of their consumers. It also significantly limits marketing expenses and increases revenue with minimum technology investment. In a nutshell, big data adds value to the business and leverages many benefits.

What are the different platforms and tools that companies use to manage big data?

Reply: As technology evolves, the number of platforms and tools available in the market keeps increasing. In particular, for Big Data, there are many open sources and other license-based platforms.

In open source, Hadoop is the largest big data platform. Hadoop is highly scalable, runs on basic hardware, and has a good ecosystem.

It is followed by the HPCC (High-Performance Computing Cluster). HPCC is also an open source big data platform. It’s a good alternative to Hadoop that features parallelism at the data, pipeline, and system level. HPCC is a high performance online query application.

In the licensing category, companies get their hands on Cloudera (CDH), Hortonworks (HDP), MapR (MDP), etc. to leverage the benefits of Big Data. CDH is the Cloudera manager for easy administration. It can be easily implemented and safely used. HDP has a dashboard with the Ambari user interface and a data analysis studio. HDP Sandbox is available for VirtualBox, VMware and Docker.

What is big data analytics and why is it important?

Reply: Big data is just big data sets. But big data analysis is different. It refers to the strategy of analyzing big data, which we call big data. Without big data analysis, the dataset will be of no value. The main goal of big data collection is to analyze and gain useful information, discover patterns, and make connections that might otherwise be invisible. Big data analysis helps achieve this goal. With this information, companies might be able to have a superior hand over their competitors and make business decisions. Additionally, big data analytics helps organizations identify new opportunities that drive businesses to be smarter, more efficient, increase profits, and more.

What is data cleansing?

Reply: Data comes in different forms. Usually it is divided into structured and unstructured data. Unfortunately, most of the data a business gets comes in an unstructured form. For example, some data will be in text format, some in the video and some in the image. Some data may be false and incorrect. Since we can’t analyze them together, a process called data cleansing takes place. Data cleansing, also known as data cleansing, is a process of removing incorrect, duplicated, or corrected data. This process is used to improve data quality by eliminating errors and irregularities.

What tools are used for big data analysis?

Reply: Big data analysis is the process of importing, sorting and analyzing data. Some of the famous tools used for this purpose are as follows,

  • Apache Hadoop
  • Apache Splunk
  • Apache hive
  • MongoDB
  • Apache Sqoop
  • Cassandra
  • Apache Channel
  • Apache Pig
Why is Hadoop so closely related to big data analytics?

Reply: Unlike many other tools, Hadoop holds a special place in big data analysis. The reason is that Hadoop has various advantages. It is efficient at processing large amounts of structured, unstructured and semi-structured data. Hadoop’s amazing data storage, processing, and collection capabilities make it easy for organizations to analyze unstructured data. Additionally, Hadoop is an open source tool that runs on community hardware, making it less expensive.

What do you know about the basic material?

Reply: Base hardware is the term used to define the minimum hardware resources required to run the Apache Hadoop framework. These are lower cost systems that are not of high quality or do not have specifications. One of the main benefits of Hadoop is that businesses can use “core hardware” to run their operations. This means companies don’t have to invest in high-end hardware specifically for big data. In a nutshell, base hardware is any hardware that supports Hadoop’s minimum requirements.

What is the purpose of A / B testing?

Reply: A / B testing is a comparative study method used to verify product performance. In this process, two or more variations of a page are presented to random users and their comments are statistically analyzed. In the end, companies find the best performing variant and promote it.

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