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50 Must Know Big Data Interview Questions and Answers 2024: For Freshers & Experienced

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19th Feb, 2024
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50 Must Know Big Data Interview Questions and Answers 2024: For Freshers & Experienced

Introduction

The demand for potential candidates is increasing rapidly in the big data technologies field. There are plenty of opportunities in this field if you aspire to be part of this domain. The most fruitful domains under big data technologies are data analytics, data science, big data engineering, and so on. For getting success in admission in big data, it is crucial to understand what kind of questions are asked in the interviews and how to answer them.

This article will help you to find a direction for the preparation of big data testing interview questions and will increase your chances of selection.

Attending a big data interview and wondering what are all the questions and discussions you will go through? Before attending a big data interview, it’s better to have an idea of the type of big data interview questions so that you can mentally prepare answers for them.

To help you out, I have created the top big data interview questions and answers guide to understand the depth and real-intend of big data interview questions.

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We’re in the era of Big Data and analytics. With data powering everything around us, there has been a sudden surge in demand for skilled data professionals. Organizations are always on the lookout for upskilled individuals who can help them make sense of their heaps of data.

big data interview questions

The number of jobs in data science is predicted to grow by 30% by 2026. This means there will be many more employment opportunities for people working with data. To make things easier for applicants and candidates, we have compiled a comprehensive list of big data interview questions

The keyword here is ‘upskilled’ and hence Big Data interviews are not really a cakewalk. There are some essential Big Data interview questions that you must know before you attend one. These will help you find your way through.

The questions have been arranged in an order that will help you pick up from the basics and reach a somewhat advanced level.

How To Prepare for Big Data Interview

Before we proceed further and understand the big data analytics interview questions directly, let us first understand the basic points for the preparation of this interview –

  • Draft a Compelling Resume – A resume is a piece of paper that reflects your accomplishments. However, you must modify your resume based on the role or position you are applying for. Your resume must reflect and compel the employer that you have gone thoroughly with the industry’s standards, history, vision, and culture. You must also mention your soft skills that are relevant to your role. 
  • Interview is a Two-sided Interaction – Apart from giving correct and accurate answers to the interview questions, you must not ignore the importance of asking your questions. Prepare a list of suitable questions in advance and ask them at favorable opportunities.
  • Research and Rehearse – You must research the most commonly asked questions which are asked in the big data analytics interviews. Prepare their answers in advance and rehearse these answers before you appear for the interview.

Big Data Interview Questions & Answers 

Here is a list of some of the most common big data interview questions to help you prepare beforehand. This list can also apply to big data viva questions, especially if you are looking to prepare for a practical viva exam. 

1. Define Big Data and explain the Vs of Big Data.

This is one of the most introductory yet important Big Data interview questions.

It also doubles as one of the most common big data practical viva questions

The answer to this is quite straightforward:

Big Data can be defined as a collection of complex unstructured or semi-structured data sets which have the potential to deliver actionable insights.


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The four Vs of Big Data are –
Volume – Talks about the amount of data.

In other words, the sheer amount of data generated, collected, and stored by organizations. 

Variety – Talks about the various formats of data.

Data comes in various forms, such as structured data (like databases), semi-structured data (XML, JSON), unstructured data (text, images, videos), and more. 

Velocity – Talks about the ever increasing speed at which the data is growing. Moreover, it denotes the speed of data generation processed in real time.
Veracity – Talks about the degree of accuracy of data available.

Big Data often involves data from multiple sources, which might be incomplete, inconsistent, or contain errors. Ensuring data quality and reliability is essential for making informed decisions. Verifying and maintaining data integrity through cleansing, validation, and quality checks become imperative to derive meaningful insights.

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2. How is Hadoop related to Big Data?

When we talk about Big Data, we talk about Hadoop. So, this is another Big Data interview question that you will definitely face in an interview.

This one also doubles as one of the most commonly asked BDA viva questions.

Hadoop is an open-source framework for storing, processing, and analyzing complex unstructured data sets for deriving insights and intelligence.

Hadoop is closely linked to Big Data because it’s a tool specifically designed to handle massive and varied types of data that are typically challenging for regular systems to manage. Hadoop’s main job is to store this huge amount of data across many computers (HDFS) and process it in a way that makes it easier to understand and use (MapReduce). 

Essentially, Hadoop is a key player in the Big Data world, helping organizations deal with their large and complex data more easily for analysis and decision-making.

3. Define HDFS and YARN, and talk about their respective components.

Now that we’re in the zone of Hadoop, the next Big Data interview question you might face will revolve around the same.

This is also among the commonly asked big data interview questions for experienced professionals. Hence, even if you have expert knowledge in this field, make sure that you prepare this question thoroughly.

The HDFS is Hadoop’s default storage unit and is responsible for storing different types of data in a distributed environment.

HDFS has the following two components:

NameNode – This is the master node that has the metadata information for all the data blocks in the HDFS.

The NameNode is like the manager of the Hadoop system. It keeps track of where all the files are stored in the Hadoop cluster and manages the file system’s structure and organization.

The NameNode is like the manager of the Hadoop system. It keeps track of where all the files are stored in the Hadoop cluster and manages the file system’s structure and organization.

DataNode – These are the nodes that act as slave nodes and are responsible for storing the data.

DataNodes are like storage units in the Hadoop cluster. They store the actual data blocks that make up the files. They follow instructions from the NameNode and store, retrieve, and replicate data as needed. YARN, short for Yet Another Resource Negotiator, is responsible for managing resources and providing an execution environment for the said processes.
The two main components of YARN are –
ResourceManager – Responsible for allocating resources to respective NodeManagers based on the needs.

It oversees allocation to various applications through the Scheduler, ensuring fair distribution based on policies like FIFO or fair sharing. Additionally, the ApplicationManager is responsible for coordinating and monitoring application execution, handling job submissions, and managing ApplicationMasters.

NodeManager – Executes tasks on every DataNode.
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It operates on individual nodes by managing resources, executing tasks within containers, and reporting container statuses to the ResourceManager. It efficiently monitors and allocates resources to tasks, ensuring optimal resource utilization while managing task execution and failure at the node level.

4. What do you mean by commodity hardware?

This is yet another Big Data interview question you’re most likely to come across in any interview you sit for.

As one of the most common big data questions, make sure you are prepared to answer the same.

Commodity Hardware refers to the minimal hardware resources needed to run the Apache Hadoop framework. Any hardware that supports Hadoop’s minimum requirements is known as ‘Commodity Hardware.’

It meets Hadoop’s basic needs and is cost-effective and scalable, making it accessible for setting up Hadoop clusters without requiring pricey specialized gear. This approach lets many businesses use regular, affordable hardware to tap into Hadoop’s powerful data processing abilities.

5. Define and describe the term FSCK.

When you are preparing big data testing interview questions, make sure that you cover FSCK. This question can be asked if the interviewer is covering HADOOP questions. 

FSCK stands for Filesystem Check. It is a command used to run a Hadoop summary report that describes the state of HDFS. It only checks for errors and does not correct them. This command can be executed on either the whole system or a subset of files.

File System (HDFS), FSCK, is a utility that verifies the health and integrity of the HDFS file system by examining its structure and metadata information. It identifies missing, corrupt, or misplaced data blocks and provides information about the overall file system’s status, including the number of data blocks, their locations, and replication status. 

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6. What is the purpose of the JPS command in Hadoop?

This is one of the big data engineer interview questions that might be included in interviews focused on the Hadoop ecosystem and tools. 

The JPS command is used for testing the working of all the Hadoop daemons. It specifically tests daemons like NameNode, DataNode, ResourceManager, NodeManager and more.
(In any Big Data interview, you’re likely to find one question on JPS and its importance.)
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This command is especially useful in verifying whether the different components of a Hadoop cluster, including the core services and auxiliary processes, are up and running.

7. Name the different commands for starting up and shutting down Hadoop Daemons.

This is one of the most important Big Data interview questions to help the interviewer gauge your knowledge of commands.

To start all the daemons:
./sbin/start-all.sh

To shut down all the daemons:
./sbin/stop-all.sh

8. Why do we need Hadoop for Big Data Analytics?

This is one of the most anticipated big data Hadoop interview questions

This Hadoop interview questions test your awareness regarding the practical aspects of Big Data and Analytics.

In most cases, Hadoop helps in exploring and analyzing large and unstructured data sets. Hadoop offers storage, processing and data collection capabilities that help in analytics.

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These capabilities make Hadoop a fundamental tool in handling the scale and complexity of Big Data, empowering organizations to derive valuable insights for informed decision-making and strategic planning.

9. Explain the different features of Hadoop.

Listed in many Big Data Interview Questions and Answers, the best answer to this is –

Open-Source – Hadoop is an open-sourced platform. It allows the code to be rewritten or modified according to user and analytics requirements.
Scalability – Hadoop supports the addition of hardware resources to the new nodes.
Data Recovery – Hadoop follows replication which allows the recovery of data in the case of any failure.
Data Locality – This means that Hadoop moves the computation to the data and not the other way round. This way, the whole process speeds up.

10. Define the Port Numbers for NameNode, Task Tracker and Job Tracker.

Understanding port numbers, configurations, and components within Hadoop clusters is a common part of big data scenario based interview questions for roles handling Hadoop administration. 

NameNode – Port 50070. 

This port allows users/administrators to access the Hadoop Distributed File System (HDFS) information and its status through a web browser.

Task Tracker – Port 50060. 

This port corresponds to TaskTracker’s web UI, providing information about the tasks it handles and allowing monitoring and management through a web browser.Job Tracker – Port 50030. 

This port is associated with the JobTracker’s web UI. It allows users to monitor and track the progress of MapReduce jobs, view job history, and manage job-related information through a web browser.

11. What do you mean by indexing in HDFS?

HDFS indexes data blocks based on their sizes. The end of a data block points to the address of where the next chunk of data blocks get stored. The DataNodes store the blocks of data while NameNode stores these data blocks.
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12. What are Edge Nodes in Hadoop?

This is one of the top big data analytics important questions which can also be asked as data engineer interview questions. Edge nodes refer to the gateway nodes which act as an interface between Hadoop cluster and the external network. These nodes run client applications and cluster management tools and are used as staging areas as well. Enterprise-class storage capabilities are required for Edge Nodes, and a single edge node usually suffices for multiple Hadoop clusters.

13. What are some of the data management tools used with Edge Nodes in Hadoop?

This Big Data interview question aims to test your awareness regarding various tools and frameworks.

Oozie, Ambari, Pig and Flume are the most common data management tools that work with Edge Nodes in Hadoop.

14. Explain the core methods of a Reducer.

This is also among the most commonly asked big data analytics interview questions

There are three core methods of a reducer. They are-

setup() – This is used to configure different parameters like heap size, distributed cache and input data.

This method is invoked once at the beginning of each reducer task before processing any keys or values. It allows developers to perform initialization tasks and configuration settings specific to the reducer task. 

reduce() – A parameter that is called once per key with the concerned reduce task.

It is where the actual data processing and reduction take place. The method takes in a key and an iterable collection of values corresponding to that key. 

cleanup() – Clears all temporary files and called only at the end of a reducer task.

The cleanup() method is called once at the end of each reducer task after all the keys have been processed and the reduce() method has completed execution. 

15. Talk about the different tombstone markers used for deletion purposes in HBase.

This Big Data interview question dives into your knowledge of HBase and its working.
There are three main tombstone markers used for deletion in HBase. They are-

Family Delete Marker – For marking all the columns of a column family.

When applied, it signifies the intent to delete all versions of all columns within that column family. This marker acts at the column family level, allowing users to delete an entire family of columns across all rows in an HBase table.

Version Delete Marker – For marking a single version of a single column.

It targets and indicates the deletion of only one version of a specific column. This delete marker permits users to remove a specific version of a column value while retaining other versions associated with the same column.

Column Delete Marker – For marking all the versions of a single column.

This delete marker operates at the column level, signifying the removal of all versions of a specific column across different timestamps or versions within an individual row in the HBase table.

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16. How can Big Data add value to businesses?

One of the most common big data interview question. In the present scenario, Big Data is everything. If you have data, you have the most powerful tool at your disposal. Big Data Analytics helps businesses to transform raw data into meaningful and actionable insights that can shape their business strategies. The most important contribution of Big Data to business is data-driven business decisions. Big Data makes it possible for organizations to base their decisions on tangible information and insights.

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Furthermore, Predictive Analytics allows companies to craft customized recommendations and marketing strategies for different buyer personas. Together, Big Data tools and technologies help boost revenue, streamline business operations, increase productivity, and enhance customer satisfaction. In fact, anyone who’s not leveraging Big Data today is losing out on an ocean of opportunities. 

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17. How do you deploy a Big Data solution?

This question also falls under one of the highest anticipated big data analytics viva questions.

You can deploy a Big Data solution in three steps:

  • Data Ingestion – This is the first step in the deployment of a Big Data solution. You begin by collecting data from multiple sources, be it social media platforms, log files, business documents, anything relevant to your business. Data can either be extracted through real-time streaming or in batch jobs.
  • Data Storage – Once the data is extracted, you must store the data in a database. It can be HDFS or HBase. While HDFS storage is perfect for sequential access, HBase is ideal for random read/write access.
  • Data Processing – The last step in the deployment of the solution is data processing. Usually, data processing is done via frameworks like Hadoop, Spark, MapReduce, Flink, and Pig, to name a few.

18. How is NFS different from HDFS?

This question also qualifies as one of the big data scenario based questions that you may be asked in an interview.

The Network File System (NFS) is one of the oldest distributed file storage systems, while Hadoop Distributed File System (HDFS) came to the spotlight only recently after the upsurge of Big Data. 

The table below highlights some of the most notable differences between NFS and HDFS:

NFSHDFS
It can both store and process small volumes of data. It is explicitly designed to store and process Big Data.
The data is stored in dedicated hardware.Data is divided into data blocks that are distributed on the local drives of the hardware. 
In the case of system failure, you cannot access the data. Data can be accessed even in the case of a system failure.
Since NFS runs on a single machine, there’s no chance for data redundancy.HDFS runs on a cluster of machines, and hence, the replication protocol may lead to redundant data.

19. List the different file permissions in HDFS for files or directory levels.

One of the common big data interview questions. The Hadoop distributed file system (HDFS) has specific permissions for files and directories. There are three user levels in HDFS – Owner, Group, and Others. For each of the user levels, there are three available permissions:

  • read (r)
  • write (w)
  • execute(x).

These three permissions work uniquely for files and directories.

For files –

  • The r permission is for reading a file
  • The w permission is for writing a file.

Although there’s an execute(x) permission, you cannot execute HDFS files.

For directories –

  • The r permission lists the contents of a specific directory.
  • The w permission creates or deletes a directory.
  • The X permission is for accessing a child directory.

20. Elaborate on the processes that overwrite the replication factors in HDFS.

In HDFS, there are two ways to overwrite the replication factors – on file basis and on directory basis.

On File Basis

In this method, the replication factor changes according to the file using Hadoop FS shell. The following command is used for this:

$hadoop fs – setrep –w2/my/test_file

Here, test_file refers to the filename whose replication factor will be set to 2.

On Directory Basis

This method changes the replication factor according to the directory, as such, the replication factor for all the files under a particular directory, changes. The following command is used for this:

$hadoop fs –setrep –w5/my/test_dir

Here, test_dir refers to the name of the directory for which the replication factor and all the files contained within will be set to 5.

21. Name the three modes in which you can run Hadoop.

One of the most common question in any big data interview. The three modes are:

  • Standalone mode – This is Hadoop’s default mode that uses the local file system for both input and output operations. The main purpose of the standalone mode is debugging. It does not support HDFS and also lacks custom configuration required for mapred-site.xml, core-site.xml, and hdfs-site.xml files. 
  • Pseudo-distributed mode – Also known as the single-node cluster, the pseudo-distributed mode includes both NameNode and DataNode within the same machine. In this mode, all the Hadoop daemons will run on a single node, and hence, the Master and Slave nodes are the same.
  • Fully distributed mode – This mode is known as the multi-node cluster wherein multiple nodes function simultaneously to execute Hadoop jobs. Here, all the Hadoop daemons run on different nodes. So, the Master and Slave nodes run separately.

22. Explain “Overfitting.”

This is one of the most common and easy big data interview questions you should not skip. It can also be a part of big data practical viva questions if you are a student in this stream. Hence, make sure you are thoroughly familiar with the answer below.

Overfitting refers to a modeling error that occurs when a function is tightly fit (influenced) by a limited set of data points. Overfitting results in an overly complex model that makes it further difficult to explain the peculiarities or idiosyncrasies in the data at hand. As it adversely affects the generalization ability of the model, it becomes challenging to determine the predictive quotient of overfitted models. These models fail to perform when applied to external data (data that is not part of the sample data) or new datasets. 

Overfitting is one of the most common problems in Machine Learning. A model is considered to be overfitted when it performs better on the training set but fails miserably on the test set. However, there are many methods to prevent the problem of overfitting, such as cross-validation, pruning, early stopping, regularization, and assembling.

23. What is Feature Selection?

This is one of the popular Big Data analytics important questions which is also often featured as data engineer interview questions. Feature selection refers to the process of extracting only the required features from a specific dataset. When data is extracted from disparate sources, not all data is useful at all times – different business needs call for different data insights. This is where feature selection comes in to identify and select only those features that are relevant for a particular business requirement or stage of data processing.

The main goal of feature selection is to simplify ML models to make their analysis and interpretation easier. Feature selection enhances the generalization abilities of a model and eliminates the problems of dimensionality, thereby, preventing the possibilities of overfitting. Thus, feature selection provides a better understanding of the data under study, improves the prediction performance of the model, and reduces the computation time significantly. 

Feature selection can be done via three techniques:

  • Filters method

In this method, the features selected are not dependent on the designated classifiers. A variable ranking technique is used to select variables for ordering purposes. During the classification process, the variable ranking technique takes into consideration the importance and usefulness of a feature. The Chi-Square Test, Variance Threshold, and Information Gain are some examples of the filters method.

  • Wrappers method

In this method, the algorithm used for feature subset selection exists as a ‘wrapper’ around the induction algorithm. The induction algorithm functions like a ‘Black Box’ that produces a classifier that will be further used in the classification of features. The major drawback or limitation of the wrappers method is that to obtain the feature subset, you need to perform heavy computation work. Genetic Algorithms, Sequential Feature Selection, and Recursive Feature Elimination are examples of the wrappers method.

  • Embedded method 

The embedded method combines the best of both worlds – it includes the best features of the filters and wrappers methods. In this method, the variable selection is done during the training process, thereby allowing you to identify the features that are the most accurate for a given model. L1 Regularisation Technique and Ridge Regression are two popular examples of the embedded method.

24. Define “Outliers.”

As one of the most commonly asked big data viva questions and interview questions, ensure that you are thoroughly prepared to answer the following.

An outlier refers to a data point or an observation that lies at an abnormal distance from other values in a random sample. In other words, outliers are the values that are far removed from the group; they do not belong to any specific cluster or group in the dataset. The presence of outliers usually affects the behavior of the model – they can mislead the training process of ML algorithms. Some of the adverse impacts of outliers include longer training time, inaccurate models, and poor outcomes. 

However, outliers may sometimes contain valuable information. This is why they must be investigated thoroughly and treated accordingly.

25. Name some outlier detection techniques.

Again, one of the most important big data interview questions. Here are six outlier detection methods:

  • Extreme Value Analysis – This method determines the statistical tails of the data distribution. Statistical methods like ‘z-scores’ on univariate data are a perfect example of extreme value analysis.
  • Probabilistic and Statistical Models – This method determines the ‘unlikely instances’ from a ‘probabilistic model’ of data. A good example is the optimization of Gaussian mixture models using ‘expectation-maximization’.
  • Linear Models – This method models the data into lower dimensions. Proximity-based Models – In this approach, the data instances that are isolated from the data group are determined by Cluster, Density, or by the Nearest Neighbor Analysis.
  • Information-Theoretic Models – This approach seeks to detect outliers as the bad data instances that increase the complexity of the dataset.
  • High-Dimensional Outlier Detection – This method identifies the subspaces for the outliers according to the distance measures in higher dimensions.

26. Explain Rack Awareness in Hadoop.

If you are a student preparing for your practical exam, make sure that you prepare Rack Awareness in Hadoop. This can also be asked as one of the BDA viva questions.

Rack Awareness is one of the popular big data interview questions. Rach awareness is an algorithm that identifies and selects DataNodes closer to the NameNode based on their rack information. It is applied to the NameNode to determine how data blocks and their replicas will be placed. During the installation process, the default assumption is that all nodes belong to the same rack.  

Rack awareness helps to:

  • Improve data reliability and accessibility.
  • Improve cluster performance.
  • Improve network bandwidth. 
  • Keep the bulk flow in-rack as and when possible.
  • Prevent data loss in case of a complete rack failure.

27. Can you recover a NameNode when it is down? If so, how?

This is one of the most common big data interview questions for experienced professionals.

Yes, it is possible to recover a NameNode when it is down. Here’s how you can do it:

  • Use the FsImage (the file system metadata replica) to launch a new NameNode. 
  • Configure DataNodes along with the clients so that they can acknowledge and refer to newly started NameNode.
  • When the newly created NameNode completes loading the last checkpoint of the FsImage (that has now received enough block reports from the DataNodes) loading process, it will be ready to start serving the client. 

However, the recovery process of a NameNode is feasible only for smaller clusters. For large Hadoop clusters, the recovery process usually consumes a substantial amount of time, thereby making it quite a challenging task. 

28. Name the configuration parameters of a MapReduce framework.

The configuration parameters in the MapReduce framework include:

  • The input format of data.
  • The output format of data.
  • The input location of jobs in the distributed file system.
  • The output location of jobs in the distributed file system.
  • The class containing the map function
  • The class containing the reduce function
  • The JAR file containing the mapper, reducer, and driver classes.

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29. What is a Distributed Cache? What are its benefits?

Any Big Data Interview Question and Answers guide won’t complete without this question. Distributed cache in Hadoop is a service offered by the MapReduce framework used for caching files. If a file is cached for a specific job, Hadoop makes it available on individual DataNodes both in memory and in system where the map and reduce tasks are simultaneously executing. This allows you to quickly access and read cached files to populate any collection (like arrays, hashmaps, etc.) in a code.

Distributed cache offers the following benefits:

  • It distributes simple, read-only text/data files and other complex types like jars, archives, etc. 
  • It tracks the modification timestamps of cache files which highlight the files that should not be modified until a job is executed successfully.

30. What is a SequenceFile in Hadoop?

In Hadoop, a SequenceFile is a flat-file that contains binary key-value pairs. It is most commonly used in MapReduce I/O formats. The map outputs are stored internally as a SequenceFile which provides the reader, writer, and sorter classes. 

There are three SequenceFile formats:

  • Uncompressed key-value records
  • Record compressed key-value records (only ‘values’ are compressed).
  • Block compressed key-value records (here, both keys and values are collected in ‘blocks’ separately and then compressed). 

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31. Explain the role of a JobTracker.

One of the common big data interview questions. The primary function of the JobTracker is resource management, which essentially means managing the TaskTrackers. Apart from this, JobTracker also tracks resource availability and handles task life cycle management (track the progress of tasks and their fault tolerance).

Some crucial features of the JobTracker are:

  • It is a process that runs on a separate node (not on a DataNode).
  • It communicates with the NameNode to identify data location.
  • It tracks the execution of MapReduce workloads.
  • It allocates TaskTracker nodes based on the available slots.
  • It monitors each TaskTracker and submits the overall job report to the client.
  • It finds the best TaskTracker nodes to execute specific tasks on particular nodes.

32. Name the common input formats in Hadoop.

Hadoop has three common input formats:

  • Text Input Format – This is the default input format in Hadoop.
  • Sequence File Input Format – This input format is used to read files in a sequence.
  • Key-Value Input Format – This input format is used for plain text files (files broken into lines).

33. What is the need for Data Locality in Hadoop?

One of the important big data interview questions. In HDFS, datasets are stored as blocks in DataNodes in the Hadoop cluster. When a  MapReduce job is executing, the individual Mapper processes the data blocks (Input Splits). If the data does is not present in the same node where the Mapper executes the job, the data must be copied from the DataNode where it resides over the network to the Mapper DataNode.

When a MapReduce job has over a hundred Mappers and each Mapper DataNode tries to copy the data from another DataNode in the cluster simultaneously, it will lead to network congestion, thereby having a negative impact on the system’s overall performance. This is where Data Locality enters the scenario. Instead of moving a large chunk of data to the computation, Data Locality moves the data computation close to where the actual data resides on the DataNode. This helps improve the overall performance of the system, without causing unnecessary delay.

34. What are the steps to achieve security in Hadoop?

In Hadoop, Kerberos – a network authentication protocol – is used to achieve security. Kerberos is designed to offer robust authentication for client/server applications via secret-key cryptography. 

When you use Kerberos to access a service, you have to undergo three steps, each of which involves a message exchange with a server. The steps are as follows:

  • Authentication – This is the first step wherein the client is authenticated via the authentication server, after which a time-stamped TGT (Ticket Granting Ticket) is given to the client.
  • Authorization – In the second step, the client uses the TGT for requesting a service ticket from the TGS (Ticket Granting Server).
  • Service Request – In the final step, the client uses the service ticket to authenticate themselves to the server. 

35. How can you handle missing values in Big Data?

Final question in our big data interview questions and answers guide. Missing values refer to the values that are not present in a column. It occurs when there’s is no data value for a variable in an observation. If missing values are not handled properly, it is bound to lead to erroneous data which in turn will generate incorrect outcomes. Thus, it is highly recommended to treat missing values correctly before processing the datasets. Usually, if the number of missing values is small, the data is dropped, but if there’s a bulk of missing values, data imputation is the preferred course of action. 

In Statistics, there are different ways to estimate the missing values. These include regression, multiple data imputation, listwise/pairwise deletion, maximum likelihood estimation, and approximate Bayesian bootstrap.

  1. What command should I use to format the NameNode?

This also falls under the umbrella of big data analytics important questions. Here’s the answer:

The command to format the NameNode is “$ hdfs namenode -format”

  1. Do you like good data or good models more? Why?

You may face these big data scenario based interview questions in interviews.

Although it is a difficult topic, it is frequently asked in big data interviews. You are asked to choose between good data or good models. You should attempt to respond to it from your experience as a candidate. Many businesses have already chosen their data models because they want to adhere to a rigid evaluation process. Good data can change the game in this situation. The opposite is also true as long as a model is selected based on reliable facts.

Answer it based on your own experience. Though it is challenging to have both in real-world projects, don’t say that having good data and good models is vital.

  1. Will you speed up the code or algorithms you use?

One of the top big data analytics important questions is undoubtedly this one. Always respond “Yes” when asked this question. Performance in the real world is important and is independent of the data or model you are utilizing in your project.

If you have any prior experience with code or algorithm optimization, the interviewer may be very curious to hear about it. It definitely relies on the previous tasks a newbie worked on. Candidates with experience can also discuss their experiences appropriately. However, be truthful about your efforts; it’s okay if you haven’t previously optimized any code. You can succeed in the big data interview if you just share your actual experience with the interviewer.

  1. What methodology do you use for data preparation?

One of the most important phases in big data projects is data preparation. There may be at least one question focused on data preparation in a big data interview. This question is intended to elicit information from you on the steps or safety measures you employ when preparing data.

As you are already aware, data preparation is crucial to obtain the information needed for further modeling. The interviewer should hear this from you. Additionally, be sure to highlight the kind of model you’ll be using and the factors that went into your decision. Last but not least, you should also go over keywords related to data preparation, such as variables that need to be transformed, outlier values, unstructured data, etc.

  1. Tell us about data engineering.

Big data is also referred to as data engineering. It focuses on how data collection and research are applied. The data produced by different sources is raw data. Data engineering assists in transforming this raw data into informative and valuable insights. 

This is one of the top motadata interview questions asked by the interviewer. Make sure to practice it among other motadata interview questions to strengthen your preparation.

  1. How well-versed are you in collaborative filtering?

A group of technologies known as collaborative filtering predict which products a specific consumer will like based on the preferences of a large number of people. It is merely the technical term for asking others for advice.

Ensure you do not skip this because it can be one of the big data questions asked in your interview.

  1. What does a block in the Hadoop Distributed File System (HDFS) mean?

When a file is placed in HDFS, the entire file system is broken down into a collection of blocks, and HDFS is completely unaware of the contents of the file. Hadoop requires blocks to be 128MB in size. Individual files may have a different value for this.

  1. Give examples of active and passive Namenodes.

The answer is that Active NameNodes operate and function within a cluster, whilst Passive NameNodes have similar data to Active NameNodes.

  1. What criteria will you use to define checkpoints?

A checkpoint is a key component of keeping the HDFS filesystem metadata up to date. By combining fsimage and the edit log, it provides file system metadata checkpoints. Checkpoint is the name of the newest iteration of fsimage.

  1. What is the primary distinction between Sqoop and distCP?

DistCP is used for data transfers between clusters, whereas Sqoop is solely used for data transfers between Hadoop and RDBMS.

Sqoop and DistCP serve different data transfer needs within the Hadoop ecosystem. Sqoop specializes in bidirectional data transfers between Hadoop and relational databases (RDBMS), allowing seamless import and export of structured data. DistCP works at the Hadoop Distributed File System (HDFS) level, breaking data into chunks and performing parallel data transfers across nodes or clusters, ensuring high-speed, fault-tolerant data movement.

You need not elaborate so much when asked one of these big data testing interview questions, but make sure that you stay one step ahead in case your interviewer asks you to.

  1. How can unstructured data be converted into structured data?

Big Data changed the field of data science for many reasons, one of which is the organizing of unstructured data. The unstructured data is converted into structured data to enable accurate data analysis. You should first describe the differences between these two categories of data in your response to such big data interview questions before going into detail about the techniques you employ to convert one form of data into another. Share your personal experience while highlighting the importance of machine learning in data transformation.

  1. How much data is required to produce a reliable result?

This can also be one of the big data engineer interview questions if you are applying for a similar job position.

Ans. Every company is unique, and every firm is evaluated differently. Therefore, there will never be enough data and no correct response. The amount of data needed relies on the techniques you employ to have a great chance of obtaining important results.

A strong data collection strategy greatly influences result accuracy and reliability. Additionally, leveraging advanced analytics and machine learning techniques can boost insights from smaller datasets, highlighting the importance of using appropriate analysis methods.

  1. Do other parallel computing systems and Hadoop differ from one another? How?

Yes, they do. Hadoop is a distributed file system. It enables us to control data redundancy while storing and managing massive volumes of data in a cloud of computers.

The key advantage of this is that it is preferable to handle the data in a distributed manner because it is stored across numerous nodes. Instead of wasting time sending data across the network, each node may process the data that is stored there.

In comparison, a relational database computer system allows for real-time data querying but storing large amounts of data in tables, records, and columns is inefficient.

  1. What is a Backup Node?

As one of the common big data analytics interview questions, prepare the answer to this well.

The Backup Node is an expanded Checkpoint Node that supports both Checkpointing and Online Streaming of File System Edits. It forces synchronization with Namenode and functions similarly to Checkpoint. The file system namespace is kept up to date in memory by the Backup Node. The backup node must store the current state from memory to generate a new checkpoint in an image file.

Read our Popular Articles related to Software Development

50. What do you mean by Google BigQuery?

This can be categorized under uncommon but nevertheless important big query interview questions. Familiarize yourself with the answer given below.

Google BigQuery is a fully managed, serverless cloud-based data warehouse provided by Google Cloud Platform. It’s designed for high-speed querying and analyzing large datasets using SQL-like queries. BigQuery offers scalable and cost-effective data storage and processing without requiring infrastructure management, making it suitable for real-time analytics and data-driven decision-making.

Are you willing to gain an advancement in your learning which can help you to make your career better with us?

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This question is often asked in the last part of the interview stage. The answer to this question varies from person to person. It depends on your current skills and qualifications and also your responsibilities towards your family. But this question is a great opportunity to show your enthusiasm and spark for learning new things. You must try to answer this question honestly and straightforwardly. At this point, you can also ask the company about its mentoring and coaching policies for its employees. You must also keep in mind that there are various programs readily available online and answer this question accordingly.

Do you have any questions for us?

As discussed earlier, the interview is a two-way process. You are also open to asking questions. But, it is essential to understand what to ask and when to ask. Usually, it is advised to keep your questions for the last. However, it also depends on the flow of your interview. You must keep a note of the time that your question can take and also track how your overall discussion has gone. Accordingly, you can ask questions from the interviewer and must not hesitate to seek any clarification.

Conclusion

We hope our Big Data Questions and Answers guide is helpful. We will be updating the guide regularly to keep you updated.

If you are interested to know more about Big Data, check out our Advanced Certificate Programme in Big Data from IIIT Bangalore.

Profile

Mohit Soni

Blog Author
Mohit Soni is working as the Program Manager for the BITS Pilani Big Data Engineering Program. He has been working with the Big Data Industry and BITS Pilani for the creation of this program. He is also an alumnus of IIT Delhi.
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Frequently Asked Questions (FAQs)

1What is Flume in data management?

Flume is a data management tool that is distributed, reliable, and flexible. It can aggregate and move huge amounts of data from various data sources. Its architecture is similar to that of the Hadoop Distributed File System (HDFS). It consists of an agent that helps the client communicate with the HDFS. This agent consists of a source, channel, and sink. The source extracts the unstructured data from the client and is based on an event-driven model. The channel is a buffer queue that temporarily stores the data, controls the speed, and prevents the loss of packets. The sink is the final data that can be stored in the HDFS. This is how Flume does data management.

2How do we detect outliers?

Outliers are unique points that are far away and abnormal compared to other data points. Sometimes, outliers are considered noise, while in some cases, they are considered to be valuable sources of information. Outliers can be detected by using the clustering technique. The points far away from all the clusters can be called outliers. Plotting techniques like box plots, scatter plots, and histograms can be used for outlier detection. Supervised methods can be used, e.g., modelling to find the normal objects and considering those points that don't fit in this model as outliers. Statistical approaches based on distance and mathematical functions also help in detecting outliers.

3What are JAR files, and what are their uses?

JAR stands for Java Archives. It consists of files that aggregate various Java class files, software, libraries, and metadata compressed and stored as a single unit. It is a cross-platform archive format. JAR contains manifest files, XML-based configurations, JSON data, texts, audio, images, etc. It works well with applets too. They are used for lossless data decompression, archiving, packing and unpacking of files, etc. Any Java Runtime Environment (JRE) can be linked to the java libraries within the JAR files. This makes code execution platform-independent.

4What is Flume in data management?

Flume is a data management tool that is distributed, reliable, and flexible. It can aggregate and move huge amounts of data from various data sources. Its architecture is similar to that of the Hadoop Distributed File System (HDFS). It consists of an agent that helps the client communicate with the HDFS. This agent consists of a source, channel, and sink. The source extracts the unstructured data from the client and is based on an event-driven model. The channel is a buffer queue that temporarily stores the data, controls the speed, and prevents the loss of packets. The sink is the final data that can be stored in the HDFS. This is how Flume does data management.

5How do we detect outliers?

Outliers are unique points that are far away and abnormal compared to other data points. Sometimes, outliers are considered noise, while in some cases, they are considered to be valuable sources of information. Outliers can be detected by using the clustering technique. The points far away from all the clusters can be called outliers. Plotting techniques like box plots, scatter plots, and histograms can be used for outlier detection. Supervised methods can be used, e.g., modelling to find the normal objects and considering those points that don't fit in this model as outliers. Statistical approaches based on distance and mathematical functions also help in detecting outliers.

6What are JAR files, and what are their uses?

JAR stands for Java Archives. It consists of files that aggregate various Java class files, software, libraries, and metadata compressed and stored as a single unit. It is a cross-platform archive format. JAR contains manifest files, XML-based configurations, JSON data, texts, audio, images, etc. It works well with applets too. They are used for lossless data decompression, archiving, packing and unpacking of files, etc. Any Java Runtime Environment (JRE) can be linked to the java libraries within the JAR files. This makes code execution platform-independent.

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