Top Big Data Interview Questions & Answers for Job Seekers

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

Big data is a revolutionary concept that has changed the tailwind of companies' improvement. The technology has shifted the way organizations collected and analyzed data, and it is expected to keep on the evolution in the future as well. The careful analysis and synthesis of big data can provide valuable insights to help them make informed decisions. While big data and big data analytics are turning to be buzzwords in the technology sphere and beyond, the demand for skilled professionals to fill big data jobs is also spiraling. An increasing number of companies are hunting for talented candidates with relevant skills to make sense of the large datasets they are dealing with. However, cracking a big data interview is not easy. Aspirants should be well-versed in many aspects of technology and know many tools and their functionalities. Even experienced professionals are often slammed with big data questions, which they stammer to answer. Therefore, Analytics Insight has listed the top 10 most asked big data interview questions and their suitable answers.

Top 10 most asked big data interview questions and their answers

How do you define Big Data?

Answer: Big data represents a complex and large datasets. As the relational database is incapable of dealing with such dense data, companies use special tools and methods to perform operations on it. Across the globe, more and more organizations are realizing the importance of big data and are using it to serve customers' needs. They help companies understand their business better and aid them to derive meaningful information from unstructured raw data collected on a regular basis. Big data is the trigger behind many companies' business decisions.

What are the five V's of big data, explain them?

Answer: The five V's of big data represents volume, velocity, variety, veracity, and value. Their definitions are as follows,

• Volume-Volume talks about the amount of data stored in a certain place or form. It is represented in petabytes.
• Velocity- Velocity represents the ever-increasing speech at which the data is growing. For example, social media is a major accelerator of big data.
• Variety- Variety defines the different types of data an entity receives. Data comes in different forms like text, audio, video, images, etc.
• Veracity- Veracity refers to the degree of accuracy of data available. Mostly, it represents the uncertainty of available data raised due to its high volume that brings incompleteness and inconsistency.
• Value- Value denotes how an organization turns its big data into value. For example, a company gets decision-making insights from big data.

What kind of value addition does big data offer to a business?

Answer: Big data is like a magic tool that could totally turn a business upside down. It has everything including patterns, trends, and insights that are needed to transform a business. However, the trick is that the content is hidden in big data and needs professionals to discover them. By making good use of big data, organizations can formulate their current and future strategies, reduce unnecessary expenses and increase efficiency. Big data also offers leniency for companies to understand the market in general and their customers in particular in a very personalized way. This helps companies provide customized solutions based on their consumers' needs. It also drastically limits the spending on marketing and increases the revenue with minimum technological investment. In a nutshell, big data adds value to the business and leverages a lot of advantages.

What are the different platforms and tools companies use to deal with big data?

Answer: As technology evolves, the number of platforms and tools available in the market keeps increasing. Especially, for big data, there are many open sources and other license-based platforms in usage.

In open-source, we have Hadoop as the biggest big data platform. Hadoop is highly scalable, runs on commodity hardware, and has a good ecosystem.

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

In the licensed category, companies get their hands on Cloudera (CDH), Hortonworks (HDP), MapR (MDP), etc. to leverage big data advantages. CDH is the Cloudera manager for easy administration. It can be easily implemented and used securely. HDP has a dashboard with Ambari UI and a data analytics studio. HDP Sandbox is available for VirtualBox, VMware, and Docker.

What is big data analytics and why is it important?

Answer: Big data just bluntly represents the large datasets. But big data analytics is different. It refers to the strategy of analyzing large volumes of data, which we call big data. Without big data analytics, the dataset will go valueless. The main purpose of gathering big data is to analyze and get useful insights, uncover patterns, and create connections that might otherwise be invisible. Big data analytics helps serve the purpose. Through this insight, businesses might be able to gain a superior hand over their rivals and make business devised decisions. Besides, big data analytics aids organizations to identify new opportunities that drive companies to be smarter, more efficient, gain higher profits, etc.

What is data cleansing?

Answer: Data comes in different forms. Generally, it is divided into structured and unstructured data. Unfortunately, most of the data a company gains falls under unstructured form. For example, some data will be in text format, some in the video, and some others in the image. Some data could be fake and incorrect. As we can't analyze them together, a process called data cleansing takes place. Data cleansing, also known as data scrubbing, is a process of removing data that is incorrect, duplicated, or corrected. This process is used for enhancing data quality by eliminating errors and irregularities.

What are the tools used for big data analytics?

Answer: Big data analytics is about importing, sorting, and analyzing data. Some of the famous tools used for the purpose are as follows,

• Apache Hadoop
• Apache Splunk
• Apache Hive
• MongoDB
• Apache Sqoop
• Cassandra
• Apache Flume
• Apache Pig

Why Hadoop is closely correlated to big data analytics?

Answer: Unlike many other tools, Hadoop holds a special place in big data analytics. The reason is that Hadoop comes with various advantages. It is effective in dealing with large amounts of structured, unstructured, and semistructured data. Hadoop's amazing storage, processing, and data collection capabilities make it easy for companies to analyze unstructured data. Besides, Hadoop is an open-source tool that runs on community hardware, which makes it less costly.

What do you know about commodity hardware?

Answer: Commodity hardware is the term used to define minimal hardware resources that are required to run the Apache Hadoop framework. It stands for lower-cost systems, which aren't high quality or specifications. One of the major advantages of Hadoop is that companies can use 'commodity hardware' for running their operations. This means that organizations don't have to invest in high-end hardware specifically for big data. In a nutshell, commodity hardware is any hardware that supports Hadoop's minimum requirements.

What is the goal of A/B testing?

Answer: A/B testing is a comparative study method used to check product performance. In this process, two or more variants of a page are presented before random users and their feedback is statistically analyzed. In the end, companies find the variation that performs better and promotes it.