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Top 7 Big Data Interview Questions & Answers

With Big Data and Data Analytics as the buzzwords of today, the demand for skilled data professionals is on the rise. An increasing number of organisations across sectors are looking to hire talented candidates with the relevant skills to make sense of a huge amount of data they are dealing with. This translates into excellent opportunities in Big Data.

List of Big Data Interview Questions and Answers

Q1. Explain the correlation between Hadoop and Big Data?

Whether you are a fresher or an experienced candidate, this is one Big Data interview question that is inevitably asked at the interviews. You need to explain that Hadoop is an open-source framework that is used for processing, storing, and analysing complex unstructured data sets for deriving actionable insights.

Q2. Define the terms HDFS and YARN along with their respective components.

This is another Hadoop related question that you might face at your next Big Data interview.

Explain that HDFS is Hadoop’s default storage unit which is mainly responsible for storing different types of data in a distributed environment. There are two components of HDFS:

Name Node – Contains the metadata information for all the data blocks in the HDFS.
Data Node – Mainly acts as substitute node and is responsible for storing the data.

YARN in the context of Big Data refers to Yet Another Resource Negotiator. It is primarily responsible for managing various resources and providing an environment for execution for the processes in question. There are two components of YARN, namely:

Resource Manager – This is responsible for allocating resources to respective Node Managers depending on their needs.
Node Manager – The main function of this is to execute tasks on every Data Node.

Q3. What do you understand by the distributed cache?

An advanced Big Data question, this is asked to most experienced professionals. You need to talk about this in detail. Distributed Cache (in Hadoop) is a dedicated service by MapReduce framework to cache files whenever required. These cached files can be accessed and read later in your code.

Q4. Explain the concept of indexing in HDFS?

This is another advanced Big Data question that experienced professionals are expected to know about. Here you need to explain that HDFS indexes data blocks depending on their sizes. Also, explain that the end of a data block points to the address of where the next set of data blocks gets stored.

Q5. What is your approach to data preparation?

The question is asked to assess your previous experience in the field. The interviewer here wants to know which steps or precautions you will take during data preparation.

Begin by explaining that data preparation is required to get important data which can then further be used for modelling purposes. Emphasize the type of model you are going to use and your reasoning behind the choice.

Do not forget to discuss other important data preparation terminologies here such as outlier values, unstructured data, transforming variables, and identifying gaps among others.

Q6. What do you understand by Edge Nodes in Hadoop?

As an experienced big data professional, you need to explain the concept in detail. Talk about edge nodes which are the gateway nodes acting as an interface between the Hadoop cluster and the external network.

Also, talk about how these nodes run various client applications and cluster management tools and are used as staging areas as well.

Q7. What is your understanding of commodity hardware?

Irrespective of the amount of experience you have in Big Data, this is one question that you can expect at the interview.

Explain that commodity hardware is the term used to define minimal hardware resources that are required to run the Apache Hadoop framework. In simpler terms, commodity hardware is any hardware that supports Hadoop’s minimum requirements.

Apart from these, some of the other Big Data interview questions you should prepare for include:

a. Explain Big Data and name the 4 V’s of Big Data.
b. How can big data analysis help in increasing business revenue?
c. What is the procedure to recover a Name Node when it is down?
d. Explain important features and core components of Hadoop.
e. Why is HDFS only suitable for large data sets only?
f. What are different steps to be followed to deploy a Big Data Solution?
g. What is the difference between NFS and HDFS?
h. What is your understanding of Rack Awareness in Hadoop? 

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