Top 100 Big Data Interview Questions

We are living in the age of Big Data and Data Analytics. With data fueling everything around us, the demand for competent data workers has skyrocketed. Organizations are continuously on the search for upskilled employees who can assist them in making sense of their massive amounts of data. Are you going to a big data interview and wondering what questions and talks you’ll have? Before going to a big data interview, it’s a good idea to get a sense of the types of questions that will be asked so that you can mentally prepare replies. To assist you, we have compiled a list of the best big data interview questions and answers to help you grasp the scope and intent of big data interview questions.

1. Explain the Vs of Big Data and define Big Data.

Big Data is a collection of unstructured or semi-structured data sets with the potential to provide meaningful insights.

The four Vs of Big Data are –
Volume – Talks about the amount of data
Variety – Talks about the various formats of data
Velocity – Talks about the ever increasing speed at which the data is growing
Veracity – Talks about the degree of accuracy of data available

2. What is the relationship between Hadoop and Big Data?

When we talk about Big Data, we talk about Hadoop. Hadoop is an open-source platform for storing, processing, and analysing large amounts of unstructured data in order to derive intelligence and insights.

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

• 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.
• DataNode – These are the nodes that act as slave nodes and are responsible for storing the data.
• YARN, short for Yet Another Resource Negotiator, is responsible for managing resources and providing an execution environment for the said processes.

4. Define Commodity Hardware

The term “commodity hardware” refers to the bare minimum of hardware requirements for running the Apache Hadoop framework. ‘Commodity Hardware’ refers to any hardware that meets Hadoop’s basic criteria.

5. What do you mean by FSCK?

Filesystem Check (FSCK) is an acronym for Filesystem Check. It’s a command that generates a Hadoop summary report that summarises HDFS’ current condition. It just looks for mistakes and does not fix them. This command can be used to run a subset of files or the entire system.

6. What is the purpose of Hadoop’s JPS command?

The JPS command is used to check the functionality of all Hadoop daemons. NameNode, DataNode, ResourceManager, NodeManager, and other daemons are all tested.

7. What are the various commands for launching and stopping Hadoop Daemons?

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

8. Describe the many characteristics of Hadoop.

• 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.

9. What do you mean by the Port Numbers for NameNode, Task Tracker and Job Tracker ?

• NameNode – Port 50070
• Task Tracker – Port 50060
• Job Tracker – Port 50030

10. What do you mean by HDFS indexing?

HDFS organises data blocks into indexes based on their size. The address of where the next chunk of data blocks will be stored is indicated at the end of a data block. The DataNodes store the data blocks, while the NameNode stores the names of the data blocks.

11. What are Hadoop’s Edge Nodes?

Edge nodes are gateway nodes that provide as a connection between the Hadoop cluster and the outside world. Client applications and cluster administration tools are run on these nodes, which also serve as staging areas. Edge Nodes must have enterprise-class storage capabilities, and a single edge node can usually handle numerous Hadoop clusters.

12. What are some of the data management technologies that are utilised with Hadoop’s Edge Nodes?

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

13. What do you mean by the core methods of a Reducer.

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.
• reduce() – A parameter that is called once per key with the concerned reduce task
• cleanup() – Clears all temporary files and called only at the end of a reducer task.

14. Discuss the various tombstone markers used in HBase for deletion purposes.

Family Delete Marker – For marking all the columns of a column family.
Version Delete Marker – For marking a single version of a single column.
Column Delete Marker – For marking all the versions of a single column.

15. How can businesses benefit from Big Data?

In today’s world, Big Data is everything. You have the most powerful instrument at your disposal if you have data. Big Data Analytics enables companies to turn raw data into actionable insights that can help them build their business plans. Big Data’s most significant contribution to business is data-driven business choices. Organizations may now make decisions based on concrete data and insights thanks to Big Data. Additionally, Predictive Analytics enables businesses to create tailored suggestions and marketing plans for various customer profiles.

16. How do you put a Big Data solution in place?

• Data Ingestion – The first stage in deploying a Big Data solution is to collect data. You start by gathering information from a variety of sources, including social networking sites, log files, business documents, and anything else important to your company. Data can be extracted in two ways: in real-time streaming and in batch processes.
• Data Storage – After the data has been extracted, it must be stored in a database. HDFS or HBase can be used. While sequential access is ideal for HDFS storage, random read/write access is ideal for HBase.
• Data Processing – Data processing is the final step in the solution’s implementation. Typically, data processing is carried out using frameworks such as Hadoop, Spark, MapReduce, Flink, and Pig, among others.

17. What is the difference between NFS and HDFS?

NFS	HDFS
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.

18. Name the different types of file permissions in HDFS for files or directory levels.

 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.

19. Explain the mechanisms that cause the replication factors in HDFS to be overwritten.

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.

20. Name the three different modes in which Hadoop can be used.

• Standalone mode — This is Hadoop’s default mode, in which both input and output operations are performed on the local file system. The standalone mode is primarily used for debugging. It lacks special settings required for mapred-site.xml, core-site.xml, and hdfs-site.xml files, as well as HDFS functionality.
• Pseudo-distributed mode — Also known as a single-node cluster, this mode includes both the NameNode and the DataNode on the same system. All Hadoop daemons will run on a single node in this manner, therefore the Master and Slave nodes will be the same.
• Fully distributed mode — Also known as a multi-node cluster, this mode allows numerous nodes to run Hadoop jobs at the same time. All of Hadoop’s daemons run on various nodes here. As a result, the Master and Slave nodes operate independently.

21. What do you mean by Overfitting ?

A modelling error called overfitting happens when a function is closely fit (affected) by a small number of data points. Overfitting produces an extremely complex model, making it even more difficult to understand the data’s peculiarities or eccentricities. It’s difficult to calculate the predictive quotient of overfitted models because it reduces the model’s generalisation capabilities. When applied to external data (data that isn’t part of the sample data) or new datasets, these models fail.

One of the most typical issues in Machine Learning is overfitting. When a model performs better on the training set but fails terribly on the test set, it is said to be overfitted. Overfitting can be avoided using a variety of techniques, including cross-validation, pruning, early halting, regularisation, and assembly.

22. Explain feature selection.

The process of extracting only the required characteristics from a dataset is referred to as feature selection. When it is taken from many sources, not all of it is always usable – different business demands necessitate different insights. This is where feature selection comes in, identifying and selecting only the elements that are important for a specific business need or stage of data processing.

The basic purpose of feature selection is to make ML models simpler so that they can be analysed and interpreted more easily. Feature selection improves a model’s generalisation ability and reduces dimensionality issues, avoiding the possibility of overfitting. As a result, feature selection aids in a better comprehension of the data under investigation, enhances the model’s prediction performance, and dramatically reduces computing time.

23. What are the three techniques of feature selection?

• Filters method
  • The features chosen in this strategy are not dependent on the classifiers chosen. To choose variables for ordering, a variable ranking technique is utilised. The variable ranking technique considers the importance and utility of a feature during the classification process. Filters include the Chi-Square Test, Variance Threshold, and Information Gain, to name a few.

• Wrappers method
  • The feature subset selection algorithm acts as a “wrapper” around the induction process in this method. The induction technique works like a “Black Box,” producing a classifier that will be used to classify features in the future. The wrappers technique has a big flaw or restriction in that it requires a lot of processing to get the feature subset. Wrappers methods include Genetic Algorithms, Sequential Feature Selection, and Recursive Feature Elimination.

• Embedded method 
  • The embedded technique incorporates the best characteristics of both the filters and wrappers methods. The variable selection is done throughout the training process with this method, allowing you to discover the most correct features for a certain model. The embedded technique is exemplified by the L1 Regularisation Technique and Ridge Regression, which are two well-known instances.

24. Explain Outliners

An outlier is a data point or observation that is significantly different from the other values in a random sample. Outliers, in other words, are numbers that are distant from the average; they do not belong to any particular cluster or group in the dataset. The presence of outliers usually has an impact on the model’s behaviour, as they might cause ML algorithms to mislead throughout the training phase. Outliers have a number of negative consequences, including increased training time, erroneous models, and bad outcomes.

Outliers, on the other hand, might sometimes include useful information. This is why they must be thoroughly researched and treated as such.

25. Name and explain some of the Outliners techniques.

• Extreme Value Analysis (EVA) identifies the data distribution’s statistical tails. Extreme value analysis is exemplified by statistical approaches such as z-scores on univariate data.
• Probabilistic and Statistical Models — This method uses a ‘probabilistic model’ of data to find the ‘unlikely cases.’ The optimization of Gaussian mixture models using ‘expectation-maximization’ is an excellent example.

• Linear Models — This strategy reduces the dimensions of the it. Proximity-based Models — In this method, Cluster, Density, or the Nearest Neighbor Analysis are used to determine which instances are isolated from the group.
• 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. How do we use Rack Awareness in Hadoop?

One of the most common big data interview questions is “rack awareness.” Rach awareness is an algorithm that uses rack information to identify and pick DataNodes that are closer to the NameNode. It is used to determine where data blocks and replicas will be put on the NameNode. The default assumption during the installation procedure is that all nodes belong to the same rack.

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

Yes, it is possible to recover a NameNode when it is down.

• By using the FsImage (the file system metadata replica) to launch a new NameNode. 
• Secondly, By Configuring 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. What are the advantages of using Rack Awareness in Hadoop?

• Enhance data accessibility and reliability.
• Improve the performance of the cluster.
• Increase the network’s bandwidth.
• As much as feasible, keep the bulk flow in-rack.
• In the event of a full rack failure, data will not be lost.

29. Name the configuration parameters of a MapReduce framework.

The configuration parameters in the MapReduce framework include:

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

30. What do you mean by Distributed Cache?

Hadoop’s distributed cache is a file caching facility provided by the MapReduce framework. If a file is cached for a given job, Hadoop makes it available on individual DataNodes in memory as well as in the system where the map and reduce processes are running concurrently. This enables you to easily access and retrieve cached files in order to populate any collection (such as arrays, hashmaps, and so on) in a programme.

31. What are the advantages of Distributed Cache?

The following are some of the advantages of using a distributed cache:

• It distributes read-only text/data files as well as more complicated types such as jars, archives, and so on.
• It keeps track of cache file modification timestamps, highlighting files that should not be updated until a job has completed successfully.

32. What do you mean by  SequenceFile in Hadoop?

A SequenceFile in Hadoop is a flat-file containing binary key-value pairs. It’s the most frequent I/O format in MapReduce. Internally, the map outputs are saved in a SequenceFile, which includes 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).

33. What is the role of JobTracker?

The JobTracker’s principal purpose is resource management, which entails overseeing the TaskTrackers. Aside from that, JobTracker keeps track of resource availability and manages the job life cycle (track the progress of tasks and their fault tolerance)

34. 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).

35. What are the features of JobTracker ?

• Firstly, It’s a procedure that takes place on a different node (not on a DataNode).
• Secondly, It talks with the NameNode in order to determine the placement.
• Thirdly, It keeps track of how MapReduce workloads are being executed.
• Next, It assigns TaskTracker nodes based on the number of slots available.
• Further, It keeps track of each TaskTracker and sends the client an overall task report.
• It locates the optimal TaskTracker nodes for doing specific activities on specific nodes.

36. What is the necessity for Hadoop Data Locality?

When a MapReduce task runs, each Mapper processes the data blocks individually (Input Splits). If the it is not present on the same node where the Mapper runs the task, it must be copied over the network from the DataNode where it is stored to the Mapper DataNode.
When a MapReduce task includes more than a hundred Mappers, and each Mapper DataNode tries to copy data from another DataNode in the cluster at the same time, network congestion occurs, lowering overall system 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.

37. What are the stages to achieving Hadoop security?

The stages 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. 

38. In Big Data, how do you deal with missing values?

The term “missing values” refers to values that are missing from a column. It happens when a variable in an observation has no data value. If missing values aren’t handled appropriately, they’ll almost certainly result in erroneous data, which will lead to wrong results. As a result, treating missing values correctly before processing datasets is highly advised. If the number of missing values is modest, the data is usually dropped; however, if there are a large number of missing values, data imputation is the preferable method.

39. What are the most frequent Hadoop input formats?

The most frequent Hadoop input formats are listed below —

• Text Input Format — In Hadoop, the Text Input Format is the default input format.
• Sequence File Input Format — This format is used to read files in a sequential order.
• The Key Value Input Format is the input format for plain text files (files that are separated into lines).

39. What are the main components of the Hadoop?

• Hadoop’s basic storage system is HDFS (Hadoop Distributed File System). HDFS stores the it files created by a cluster of commodity hardware. Even if the hardware breaks, it can keep it in a secure manner.
• Hadoop MapReduce – The Hadoop layer in charge of it processing is MapReduce. It creates an application to process HDFS-stored unstructured and structured data. It is in charge of breaking into discrete tasks in order to handle large amounts of data in parallel. Map and Reduce are the two phases of the processing. The first phase of processing, Map, specifies sophisticated logic code, and the second phase, Reduce, specifies simple logic code.

40. What is Standalone (Local) Mode in Hadoop?

Hadoop runs in a local mode by default, that is, on a single non-distributed node. This mode performs input and output operations using the local file system. This mode is used for debugging because it does not support HDFS. In this manner, no custom setup is required for configuration files.

41. Explain the meaning of Pseudo-Distributed Mode in Hadoop?

Hadoop works on a single node in the pseudo-distributed mode, just like it does in the Standalone mode. Each daemon runs in its own Java process in this mode. Because all of the daemons execute on the same node, the Master and Slave nodes are the same node

42. What are the configuration parameters in a “MapReduce” program?

The main configuration parameters in “MapReduce” framework are:

• Input locations of Jobs in the distributed file system
• Output location of Jobs in the distributed file system
• The input format
• The output format
• The class which contains the map function
• The class which contains the reduce function
• JAR file which contains the mapper, reducer and the driver classes

43.What do you mean by Fully – Distributed Mode in Hadoop?

All daemons execute on separate individual nodes in the fully distributed mode, forming a multi-node cluster. For Master and Slave nodes, there are separate nodes.

44. What is a block in Hadoop 1 and Hadoop 2, and what is its default size? Is it possible to adjust the block size?

• In a hard disc, blocks are the smallest continuous data storage units. HDFS stores blocks across a Hadoop cluster.
• In Hadoop 1, the default block size is 64 MB.
• Hadoop 2’s default block size is 128 MB.
• Yes, the parameter dfs.block.size in the hdfs-site.xml file can be used to adjust block size.

45. What are the different Hadoop configuration files?

• core-site.xml – This configuration file contains Hadoop core configuration settings, for example, I/O settings, very common for MapReduce and HDFS. It uses hostname a port.
• mapred-site.xml – This configuration file specifies a framework name for MapReduce by setting mapreduce.framework.name
• hdfs-site.xml – This configuration file contains HDFS daemons configuration settings. It also specifies default block permission and replication checking on HDFS.
• yarn-site.xml – This configuration file specifies configuration settings for ResourceManager and NodeManager.

46. What are the basic parameters of a Mapper?

The basic parameters of a Mapper are

• LongWritable and Text
• Text and IntWritable

47. What do you mean by Sequencefileinputformat?

Hadoop uses a specific file format which is known as Sequence file. The sequence file stores data in a serialized key-value pair. Sequencefileinputformat is an input format to read sequence files.

48. What is the major difference between structured or unstructured data?

Structured Data refers to that can be recorded in standard database systems in the form of rows and columns, such as online purchase transactions. Semi-structured data refers to data that can only be stored partially in standard database systems, such as data in XML records. Unstructured data is unorganised and raw data that cannot be classified as semi-structured or structured data. Unstructured data includes Facebook updates, Twitter tweets, reviews, weblogs, and so on.

49. Mention a business scenario in which you used the Hadoop Ecosystem.

You can describe how you used Cloudera and Hortonworks Hadoop distributions in your organisation, whether in a standalone environment or in the cloud. Mention how you set up the required number of nodes, tools, services, and security features like SSL, SASL, and Kerberos, among other things. After you’ve built up the Hadoop cluster, describe how you collected data from APIs, SQL-based databases, and other sources and placed it in HDFS (the storage layer), how you cleaned and validated the data, and the series of ETLs you used to extract KPIs in the appropriate format.

50. Explain the process of copying data between clusters.

Through DistCP, HDFS provides a distributed data copying facility from source to destination. Inter cluster data copying refers to data copying that occurs within the Hadoop cluster. Both the source and destination must use the same or compatible version of Hadoop for DistCP to work.

51. What is the procedure for changing files in HDFS at random locations?

HDFS does not enable flexible file offsets or multiple writers; instead, files are written in append only format by a single writer, which means that writes to a file in HDFS are always made at the end of the file.

52. Explain how the HDFS indexing mechanism works.

The block size affects the indexing process in HDFS. The last part of the data chunk is stored in HDFS, and it also directs to the address where the next part of the data chunk is stored.

53.What happens when a NameNode is empty?

There is no such thing as a NameNode that is devoid of data. If it’s a NameNode, it should have some sort of information.

54. When a user submits a Hadoop job when the NameNode is unavailable, does the job get put on hold or fails?

When the NameNode is offline, the Hadoop task fails.

55. Describe the partitioning, shuffle, and sorting phases.

• Shuffle Phase-After completing the first map jobs, the nodes conduct numerous more map tasks while also exchanging intermediate outputs with the reducers as needed. Shuffling refers to the process of transferring intermediate outputs from map jobs to the reducer.
• Prior to passing the intermediate keys to the reducer, Hadoop MapReduce arranges the collection of intermediate keys on a single node.
• Partitioning Phase-Partitioning is the process of determining which intermediate keys and values will be received by each reducer instance.

56. Define the Row key.

RowKey is a unique identifier for each row in an HBase table. It’s utilised for logically grouping cells and ensuring that all cells with the same RowKeys are on the same server. Internally, RowKey is treated as a byte array.

57. What are the various HBase operating commands at the record and table levels?

• Record Level Operational Commands in HBase are –put, get, increment, scan and delete.
• Table Level Operational Commands in HBase are-describe, list, drop, disable and scan.

58. What is the difference between the RDBMS and HBase data models?

• HBase is a schema-less model, whereas RDBMS is a schema-based database.
• In-built partitioning is not available in RDBMS, but automatic partitioning is available in HBase.
• HBase stores it that has been de-normalized, whereas RDBMS saves data that has been normalised.

59. What are the various catalogue tables in HBase?

ROOT and META are the two most significant catalogue tables in HBase. The ROOT table keeps track of where the META table is, while the META table stores all of the system’s regions.

60. What exactly are column families? What happens if you change ColumnFamily’s block size on an already-populated database?

A key known as column Family represents the logical deviation of it . The basic unit of physical storage on which compression features can be applied is called a column family. When the block size of a column family is changed in an already populated database, the old data will stay in the old block size, while new data will take the new block size. When data is compressed, the old data is resized to fit the new block size, allowing existing data to be read appropriately.

61. What’s the difference between Hive and HBase?

HBase and Hive are two fundamentally separate Hadoop-based technologies: Hive is a Hadoop-based warehouse infrastructure, and HBase is a Hadoop-based NoSQL key-value store. Hive makes it easier for SQL experts to conduct MapReduce jobs, whereas HBase has four main operations: put, get, scan, and delete. Hive is a good choice for analytical querying of data acquired over time, while HBase is good for real-time querying of huge data.

62. Explain the HBase row deletion method.

When you run a delete command in HBase through the HBase client, the data isn’t actually erased from the cells; instead, a tombstone marker is set to make the cells invisible. During compaction, the deleted cells are eliminated at regular intervals.

63. What are the various sorts of tombstone markers that can be deleted in HBase?

In HBase, there are three sorts of tombstone markers for deletion:

• Family Delete Marker- This marker deletes all columns associated with a column family.
• Version Delete Marker—This marker denotes that a column has just one version.
• Column Delete Marker—This marker marks all of a column’s versions.

64. Define Apache Hadoop YARN?

YARN is a powerful and efficient feature rolled out as a part of Hadoop 2.0.YARN is a large scale distributed system for running big data applications.

65. Is YARN a Hadoop MapReduce replacement?

YARN, also known as Hadoop 2.0 or MapReduce 2, is not a substitute for Hadoop, rather it is a more powerful and efficient technology that supports MapReduce.

66. What are the various methods for dealing with Big Data?

Because Big Data provides a firm with a competitive advantage over its competitors, a company can chose to use it to meet its needs and streamline its numerous business activities to meet its goals. As a result, the ways to dealing with it must be decided based on your business needs and available budgetary resources.

67.What is the purpose of Hadoop in Big Data analytics?

Hadoop is a Java-based open-source framework for processing large amounts of data on a cluster of commodity hardware. It also enables the execution of a variety of exploratory data analysis activities on entire datasets without sampling. Hadoop has the following characteristics that make it a must-have for Big Data:

• Obtaining information
• Storage
• Processing
• It is self-contained

68. Name a few of the most important tools.

• Firstly, NodeXL
• Secondly, KNIME
• Next, Tableau
• Further, Solver
• Followed by, OpenRefine
• GUI for Rattle
• Last but not least, Qlikview

69. What exactly do you mean when you say logistic regression?

Logistic regression, often known as the logit model, is a strategy for predicting a binary outcome from a linear combination of predictor variables.

70. What is your understanding of collaborative filtering?

Collaborative filtering is a set of technologies that predict which things a specific customer will like based on the preferences of a group of people. It’s simply a technical term for asking people for their opinions.

71. How do you go about gathering data?

Because preparation is such an important part of big data projects, the interviewer may be curious about how you plan to clean and transform raw data before processing and analysis. You should discuss the model you’ll be employing, as well as the logical reasoning behind it, in response to one of the most typically asked big data interview questions. You should also talk about how your actions will assist you achieve greater scalability and faster utilisation.

72. What’s the best way to turn unstructured data into structured data?

One of the primary reasons that Big Data transformed the data science sector was the ability to organise unstructured. To ensure proper analysis, unstructured data is turn into structured data. When responding to such data interview questions, you should first define the two categories of data and then outline the strategies you employ to convert one form to another. While sharing your practical experience, emphasise the importance of machine learning in data transformation.

73. How would you deal with those data issues?

• Using data management software that gives you a clear picture of your data assessment
• Using technologies to delete any data that is of poor quality
• Performing periodic data audits to guarantee that user privacy is protect
• Combining datasets and making them useful with AI-powered tools or software as a service (SaaS) offerings

74.What does it mean to sample in clusters?

Cluster sampling is a form of sampling that allows the researcher to divide the population into distinct groups called clusters. The data is then evaluate from the sample clusters using a simple cluster sample drawn from the population.

75. Why is big data so vital for businesses?

Big data is significant because it allows firms to gain insight into information such as:

• Firstly, Cost-cutting
• Secondly, Product or service enhancements
• Next, To have a better understanding of customer behaviour and markets
• Further, Decision-making that is effective
• Last but not least, to improve our competitiveness

76. What does it mean to implement a big data solution?

• Big data solutions are first executed on a limited scale, based on a business-appropriate concept. The business solution is scaled up from the result, which is a prototype solution. The following are some of the industry’s best practises:
• To have a clear understanding of the project’s goals and to collaborate wherever possible.
• Getting the appropriate information from the proper people
• Make sure the data isn’t distorted, as this can lead to incorrect findings.
• Consider hybrid ways in processing by including data from both structured and unstructured categories, as well as internal and external data sources.

77. What is the definition of speculative execution?

• It’s an optimization approach, to be sure.
• The computer system performs several functions that aren’t always require.
• Branch prediction in pipelined processors and optimistic concurrency control in database systems are examples of where this approach is use.

78.What exactly do you mean when you say “logistic regression”?

Logistic Regression, often known as the logit model, is a strategy for predicting a binary result from a linear combination of predictor variables.

79. What is a data analyst’s job description?

• Assisting marketing executives in determining which items are the most profitable based on season, consumer type, geography, and other characteristics.
  External trends in terms of geography, demographics, and specific items are being tracked.
• Ascertain that customers and workers have a good working relationship.
• Defining the best staffing strategies to meet the demands of executives seeking decision help.

80. What are the components of YARN?

 The two main components of YARN (Yet Another Resource Negotiator) are:

• Resource Manager
• Node Manager

81. Data Profiling vs. Data Mining: What’s the Difference?

• Profiling of data: It is the process of examining particular data properties. It primarily focuses on supplying useful properties such as data type, frequency, length, and null value occurrence.
• Data mining refers to the process of analysing data in order to discover previously unknown relationships. It primarily focuses on finding anomalous records, determining dependencies, and doing cluster analysis.

82. What is the Data Analysis Process?

It is a process of gathering, cleansing, interpreting, manipulating, and modelling in order to get business insights and provide reports. The numerous processes involved in the process are depicted in the graphic below.

• Collect information: The information is gathered from a variety of sources and kept in order to be cleansed and process. All missing values and outliers are removed in this step.
  
• Analyze the Information: The next step is to analyse the it once it has been prepared. A model is run several times to see whether it can be improve. The model is next validated to see if it satisfies the business requirements.
• Create Reports: Finally, the model is implemented, and then reports thus generated are passed onto the stakeholders.

83. What are some of the difficulties you’ve encountered while analysing data?

The following are some of the most common issues in a Data Analytics project:

• Firstly, Data is of poor quality, with several missing and incorrect values.
• Next, Timelines that are unrealistic and expectations from corporate stakeholders
• Followed by, Blending/integrating data from numerous sources is difficult, especially when there are no standard parameters and norms.
• Last but not least, Inadequate tool and data architecture selection to meet analytics goals in a timely manner.

84. What do you mean when you say “normal distribution”?

• It’s a continuous symmetric distribution with the same mean, median, and mode. It is normal because it is a symmetric distribution.
  On the y-axis, the distribution is symmetric and is bisect by the mean.
• The curve’s tails stretch beyond infinity.
• Its mean and standard deviation set it apart from the rest of the normal probability distribution family.
• The mean, which is also the median and mode of the distribution, is the highest point of the distribution.

85. Explain the core methods of a Reducer.

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

1. setup() – Configures different parameters like distributed cache, heap size, and input data.
2. reduce() – A parameter that is called once per key with the concern reduce task
3. cleanup() – Clears all temporary files and called only at the end of a reducer task.

86. What are the various Hadoop vendor-specific distributions?

Cloudera, MAPR, Amazon EMR, Microsoft Azure, IBM InfoSphere, and Hortonworks are some of the vendor-specific Hadoop distributions (Cloudera).

87. Why does HDFS have fault tolerance?

Because it replicates data across multiple DataNodes, HDFS is fault-tolerant. A block of data is duplicated on three DataNodes by default. Different DataNodes are used to hold the data blocks. Data can still be obtain from other DataNodes if one node fails.

88. What are the two kinds of metadata stored on a NameNode server?

The two types of metadata that a NameNode server holds are:

• Metadata in Disk – This contains the edit log and the FSImage
• Metadata in RAM – This contains the information about DataNodes

89. How many input splits would HDFS make and what size would each input split be if you had a 350 MB input file?

Each block in HDFS is partitioned into 128 MB by default. Except for the last block, all of the blocks will be 128 MB in size. There are three input splits in total for a 350 MB input file. Each division is 128 MB, 128 MB, and 94 MB in size.

90. In Hadoop, what do you mean by Edge Nodes?

As a seasoned big data expert, you must thoroughly convey the notion. Edge nodes are gateway nodes that provide as a connection between the Hadoop cluster and the outside network. Also, discuss how these nodes are utilised as staging areas and run various client applications and cluster administration tools.

91. What happens if a Resource Manager in a high-availability cluster fails while running an application?

There are two Resource Managers in a high-availability cluster: one active and the other standby. In a high availability cluster, if a Resource Manager fails, the standby is elected as active and the Application Master is instructed to abort. By utilising the container statuses supplied by all node managers, the Resource Manager is able to recover its running state.

92. Is it possible for NameNode and DataNode to be commodity hardware?

DataNodes, which store data and are required in huge numbers, are commodity hardware like personal computers and laptops, according to the wise answer to this question. However, based on your experience, you can deduce that NameNode is the master node, and it contains metadata for all HDFS blocks. Because it necessitates a large amount of memory (RAM), NameNode must be a high-end system with plenty of RAM.

93.What’s the distinction between a “HDFS Block” and a “Input Split”?

The physical divide of the data is called “HDFS Block,” whereas the logical separation is called “Input Split.” HDFS divides data into blocks for storage, whereas MapReduce divides data into input splits and assigns them to mapper functions for processing.

94.What exactly is a “Combiner”?

A “Combiner” is a little “reducer” that executes the “reduce” task locally. It takes input from a “mapper” on a certain “node” and delivers the output to a “reducer.” By lowering the amount of data that must be delivered to the “reducers,” “combiners” help to improve the efficiency of “MapReduce.”

95. What are the sources of Unstructured data in Big Data?

The following are the sources of unstructured data:

• Documents and text files
• Logs from the server’s website and application
• Data from sensors
• Audio, video, and image files
• Emails
• Data from social media

96. Mention some statistical methods that a data analyst will need?

The following are some statistical methods:

• Firstly, Markov chain
• Secondly, Optimization in mathematics
• Next, Techniques for imputation
• Further, Algorithm of the Simplex
• Followed by, The Bayesian Approach
• Last, Statistics on spatial and cluster processes are ranked.

97. What does the P-value mean in terms of statistical data?

The main purpose of P-value in statistics is to determine the significance of results after a hypothesis test.

• The P-value, which is always between 0 and 1, allows readers to form inferences.
• P-values greater than 0.05 indicate that there is insufficient evidence to reject the null hypothesis.
• A P-value of 0.05 indicates significant evidence against the null hypothesis, implying that it can be discarded.
• The marginal value of 0.05 indicates that it is feasible to go either way.

98. When it comes to data mining and data profiling, what’s the difference?

The following is the major distinction between data mining and data profiling:

• Data profiling aims to analyse individual attributes such as price variations, distinct prices and their frequency, the occurrence of null values, data type, length, and so on in real time.
• Data mining is concerned with dependencies, sequence finding, maintaining relationships between several attributes, cluster analysis, and the detection of odd records, among other things.

99. What is the definition of data cleansing?

Data cleansing, often known as data scrubbing, is the act of removing inaccurate, duplicated, or corrupted data. This method is used to improve data quality by removing errors and inconsistencies.

100. What are some Big Data tools?

In Big Data technology, there are a variety of tools for importing, sorting, and analysing data. The following is a list of some tools:

• Firstly, Apache Hive
• Secondly, Apache Spark
• Thirdly, MongoDB
• Next, MapReduce
• Followed by, Apache Sqoop
• Further, Cassandra
• Apache Flume
• Last but not least, Apache Pig

Conclusion

We’ve covered the finest Big Data interview questions for both beginners and experts above. We are aware, however, of the importance of the interview process in obtaining a decent job. As a result, you must combine your knowledge and talents in order to answer all of the questions and pass the interview. Simply go through the questions above to improve your understanding. I hope this information is useful to you on your cloud adventure, and do let me know if you have any questions.