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Big Data Hadoop professionals are among the highest-paid IT professionals in the world today. Besides, the demand for these professionals is only increasing with each passing day since most organizations receive large amounts of data on a regular basis. In this Big Data Hadoop Interview Questions blog, you will come across a compiled list of the most probable Big Data Hadoop questions that recruiters ask in the industry. Check out these popular Big Data Hadoop interview questions mentioned below:

Q1. What are the differences between Hadoop and Spark?
Q2. What are the real-time industry applications of Hadoop?
Q3. How is Hadoop different from other parallel computing systems?
Q4. What is Hadoop and what are its components?
Q5. What is HBase?
Q6. In what all modes Hadoop can be run?
Q7. What is the difference between RDBMS and Hadoop?
Q8. Explain the major difference between HDFS block and InputSplit.
Q9. What is distributed cache? What are its benefits?
Q10. What is a Combiner?

This Big Data Hadoop Interview Questions blog is categorized in the following three parts:
1. Basic

2. Intermediate

3. Advanced

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Basic Interview Questions

1. What are the differences between Hadoop and Spark?

Criteria Hadoop Spark
Dedicated storage HDFS None
Speed of processing Average Excellent
Libraries Separate tools available Spark Core, SQL, Streaming, MLlib, and GraphX

2. What are the real-time industry applications of Hadoop?

Hadoop, well known as Apache Hadoop, is an open-source software platform for scalable and distributed computing of large volumes of data. It provides rapid, high performance, and cost-effective analysis of structured and unstructured data generated on digital platforms and within the enterprise. It is used in almost all departments and sectors today.

Here are some of the instances where Hadoop is used:

  • Managing traffic on streets
  • Streaming processing
  • Content management and archiving e-mails
  • Processing rat brain neuronal signals using a Hadoop computing cluster
  • Fraud detection and prevention
  • Advertisements targeting platforms are using Hadoop to capture and analyze click stream, transaction, video, and social media data
  • Managing content, posts, images, and videos on social media platforms
  • Analyzing customer data in real time for improving business performance
  • Public sector fields such as intelligence, defense, cyber security, and scientific research
  • Getting access to unstructured data such as output from medical devices, doctor’s notes, lab results, imaging reports, medical correspondence, clinical data, and financial data

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3. How is Hadoop different from other parallel computing systems?

Hadoop is a distributed file system that lets you store and handle massive amounts of data on a cloud of machines, handling data redundancy.

The primary benefit of this is that since data is stored in several nodes, it is better to process it in a distributed manner. Each node can process the data stored on it instead of spending time on moving the data over the network.

On the contrary, in the relational database computing system, we can query data in real time, but it is not efficient to store data in tables, records, and columns when the data is huge.

Hadoop also provides a scheme to build a column database with Hadoop HBase for runtime queries on rows.

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4. What is Hadoop and what are its components?

Apache Hadoop is the solution for dealing with Big Data. It is an open-source framework that offers several tools and services to store, manage, process, and analyze Big Data. This allows organizations to make significant business decisions in an effective and efficient manner, which was not possible with traditional methods and systems.

Listed below are the main components of Hadoop:

  • HDFS: HDFS or Hadoop Distributed File System is Hadoop’s storage unit.
  • MapReduce: MapReduce the Hadoop’s processing unit.
  • YARN: YARN is the resource management unit of Apache Hadoop.

5. What is HBase?

Apache HBase is a distributed, open-source, scalable, and multidimensional database of NoSQL that is based on Java. It runs on HDFS and offers Google BigTable-like abilities and functionalities to Hadoop. Moreover, its fault-tolerant nature helps in storing large volumes of sparse data sets. It gets low latency and high throughput by offering faster access to large datasets for read/write functions.

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6. In what all modes Hadoop can be run?

Hadoop can be run in three modes:

Modes in Hadoop

  • Standalone mode:The default mode of Hadoop, it uses local file system for input and output operations. This mode is mainly used for the debugging purpose, and it does not support the use of HDFS. Further, in this mode, there is no custom configuration required for mapred-site.xml, core-site.xml, and hdfs-site.xml files. This mode works much faster when compared to other modes.
  • Pseudo-distributed mode (Single-node Cluster):In this case, you need configuration for all the three files mentioned above. In this case, all daemons are running on one node, and thus both Master and Slave nodes are the same.

Fully distributed mode (Multi-node Cluster): This is the production phase of Hadoop (what Hadoop is known for) where data is used and distributed across several nodes on a Hadoop cluster. Separate nodes are allotted as Master and Slave.

 

7. What is the difference between RDBMS and Hadoop?

Following are some of the differences between RDBMS (Relational Database Management) and Hadoop based on various factors:

RDBMS Hadoop
Data Types It relies on structured data and the data schema is always known. Hadoop can store structured, unstructured, and semi-structured data.
Cost Since it is licensed, it is a paid software. It is a free open-source framework.
Processing It offers little to no capabilities for processing. It supports data processing for data distributed in a parallel manner across the cluster.
Read vs Write Schema It follows ‘schema on write’, allowing the validation of schema to be done before data loading. It supports the policy of schema on read.
Read/Write Speed Reads are faster since the data schema is known. Writes are faster since schema validation does not take place during HDFS write.
Best Use Case It is used for Online Transactional Processing (OLTP) systems. It is used for data analytics, data discovery, and OLAP systems.

8. Explain the major difference between HDFS block and InputSplit.

In simple terms, a block is the physical representation of data while split is the logical representation of data present in the block. Split acts as an intermediary between the block and the mapper.
Suppose we have two blocks:

Block 1: ii nntteell

Block 2: Ii ppaatt

Now considering the map, it will read Block 1 from ii to ll but does not know how to process Block 2 at the same time. Here comes Split into play, which will form a logical group of Block 1 and Block 2 as a single block.

It then forms a key–value pair using InputFormat and records reader and sends map for further processing with InputSplit. If you have limited resources, you can increase the split size to limit the number of maps. For instance, if there are 10 blocks of 640 MB (64 MB each) and there are limited resources, you can assign ‘split size’ as 128 MB. This will form a logical group of 128 MB, with only 5 maps executing at a time.

However, if the ‘split size’ property is set to false, the whole file will form one InputSplit and is processed by a single map, consuming more time when the file is bigger.

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Intermediate Interview Questions

9. What is distributed cache? What are its benefits?

Distributed cache in Hadoop is a service by MapReduce framework to cache files when needed.

Once a file is cached for a specific job, Hadoop will make it available on each DataNode both in system and in memory, where map and reduce tasks are executing. Later, you can easily access and read the cache file and populate any collection (like array, hashmap) in your code.

Distributed Cache

Benefits of using distributed cache are as follows:

  • It distributes simple, read-only text/data files and/or complex types such as jars, archives, and others. These archives are then un-archived at the slave node.
  • Distributed cache tracks the modification timestamps of cache files, which notify that the files should not be modified until a job is executed.

Learn more about MapReduce from this MapReduce Tutorial now!

10. What is a Combiner?

A Combiner is a mini version of a reducer that is used to perform local reduction processes. The mapper sends the input to a specific node of the Combiner which later sends the respective output to the reducer. It also reduces the quantum of data that needs to be sent to the reducers, improving the efficiency of MapReduce.

11. What are the various components of Apache HBase?

There are three main components of Apache HBase that are mentioned below:

  • HMaster: It manages and coordinates the Region Server just like NameNode manages DataNodes in HDFS.
  • Region Server: It is possible to divide a table into multiple regions and the Region Server makes it possible to serve a group of regions to the clients.
  • ZooKeeper: Zookeeper is a coordinator in the distributed environment of HBase. It communicates through the sessions to maintain the state of the server in the cluster.

12. What are the components of Apache HBase’s Region Server?

Following are the components of the Region Server of HBase:

  • BlockCache: It resides on Region Server and stores data in the memory that is read frequently.
  • WAL: WAL or Write Ahead Log is a file that is attached to each Region Server located in the distributed environment.
  • MemStore: MemStore is the write cache that stores the input data before it is stored in the disk or permanent memory.
  • HFile: HDFS stores the HFile that stores the cells on the disk.

13. What are the various schedulers in YARN?

Mentioned below are the numerous schedulers that are available in YARN:

  • FIFO Scheduler: FIFO or first in, first out scheduler places all the applications in a single queue and executes them in the same order as their submission. As it can block short applications due to long-running applications, it is less efficient and desirable by professionals.
  • Capacity Scheduler: A different queue makes it possible to start executed short-term jobs as soon as they are submitted. Unlike in FIFO Scheduler, the long-term tasks are completed later in Capacity Scheduler.
  • Fair Scheduler: Fair Scheduler, as the name suggests, works fairly. It balances the resources dynamically between all the running jobs and is not required to reserve a specific capacity for them.

14. Explain the difference between NameNode, Checkpoint NameNode, and Backup Node.

  • NameNode is the core of HDFS that manages the metadata—the information of which file maps to which block locations and which blocks are stored on which DataNode. In simple terms, it’s the data about the data being stored. NameNode supports a directory tree-like structure consisting of all the files present in HDFS on a Hadoop cluster. It uses the following files for namespace:
    • fsimage file: It keeps track of the latest Checkpoint of the namespace.
    • edits file: It is a log of changes that have been made to the namespace since Checkpoint.

    NameNode

  • Checkpoint NameNode has the same directory structure as NameNode and creates Checkpoints for namespace at regular intervals by downloading the fsimage, editing files, and margining them within the local directory. The new image after merging is then uploaded to NameNode. There is a similar node like Checkpoint, commonly known as the Secondary Node, but it does not support the ‘upload to NameNode’ functionality.Checkpoint NameNode
  • Backup Node provides similar functionality as Checkpoint, enforcing synchronization with NameNode. It maintains an up-to-date in-memory copy of the file system namespace and doesn’t require getting hold of changes after regular intervals. The Backup Node needs to save the current state in-memory to an image file to create a new Checkpoint.Backup Node

Go through this HDFS Tutorial to know how the distributed file system works in Hadoop!

15. What are the most common input formats in Hadoop?

There are three most common input formats in Hadoop:

  • Text Input Format: Default input format in Hadoop
  • KeyValue Input Format: Used for plain text files where the files are broken into lines
  • Sequence File Input Format: Used for reading files in sequence

16. How to execute a Pig script?

The three methods listed below enables users to execute a Pig script:

  • Grunt shell
  • Embedded script
  • Script file

17. What is Apache Pig and why is it preferred over MapReduce?

Pig is a Hadoop-based platform that allows professionals to analyze large sets of data and represent them as data flows. Pig reduces the complexities that are required while writing a program in MapReduce, giving it an edge over MapReduce.

Following are some of the reasons why Pig is more preferable as compared to MapReduce:

  • While Pig is a language for high-level data flow, MapReduce paradigm for low-level data processing
  • Without the need to write complex Java code in MapReduce, a similar result can easily be achieved in Pig
  • Pig approximately reduces the code length by 20 times, reducing the time taken for development by about 16 times than MapReduce
  • Pig offers built-in functionalities to perform numerous operations, including sorting, filters, joins, ordering, and more which are extremely difficult to perform in MapReduce
  • Unlike MapReduce, Pig provides various nested data types, such as bags, maps, and tuples.

18. Mention some commands in YARN to check application status and to kill an application.

The YARN commands are mentioned below as per their functionalities:

1. yarn application - status ApplicationID

This command allows professionals to check the application status.

2. yarn application - kill ApplicationID

The command mentioned above enables users to kill or terminate a particular application.

19. What are the different components of Hive query processors?

There are numerous components that are used in Hive query processors and they are mentioned below:

  • User-defined functions
  • Semantic analyzer
  • Optimizer
  • Physical plan generation
  • Logical plan generation
  • Type checking
  • Execution engine
  • Parser
  • Operators

20. Define DataNode. How does NameNode tackle DataNode failures?

DataNode stores data in HDFS; it is a node where actual data resides in the file system. Each DataNode sends a heartbeat message to notify that it is alive. If the NameNode does not receive a message from the DataNode for 10 minutes, the NameNode considers the DataNode to be dead or out of place and starts the replication of blocks that were hosted on that DataNode such that they are hosted on some other DataNode. A BlockReport contains a list of the all blocks on a DataNode. Now, the system starts to replicate what were stored in the dead DataNode.

The NameNode manages the replication of the data blocks from one DataNode to another. In this process, the replication data gets transferred directly between DataNodes such that the data never passes the NameNode.

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21. What is the significance of Sqoop’s eval tool?

The eval tool in Sqoop enables users to carry out user-defined queries on the corresponding database servers and check the outcome in the console.

22. What are the differences between Relational Databases and HBase?

The differences between Relational Databases and HBase are mentioned below:

Relational Database HBase
It is schema-based. It has no schema.
It is row-oriented. It is column-oriented.
It stores normalized data. It stores denormalized data.
It consists of thin tables. It consists of sparsely populated tables.
There is no built-in support or provision for automatic partitioning. It supports automated partitioning.

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23. What are the core methods of a Reducer?

The three core methods of a Reducer are as follows:

  1. setup(): This method is used for configuring various parameters such as input data size and distributed cache.
    public void setup (context)
  2. reduce(): Heart of the Reducer is always called once per key with the associated reduced task.
    public void reduce(Key, Value, context)
  3. cleanup(): This method is called to clean the temporary files, only once at the end of the task.
    public void cleanup (context)

Advanced Interview Questions

24. What are the differences between MapReduce and Pig?

The differences between MapReduce and Pig are mentioned below:

MapReduce Pig
It has more lines of code as compared to Pig. It has fewer lines of code.
It is a low-level language that makes it difficult to perform operations like join.  This high-level language makes it easy to perform join and other similar operations.
The compiling process is time-consuming. During execution, all the Pig operators are internally converted into a MapReduce job.
A MapReduce program that is written in a particular version of Hadoop may not work in others. It works in all Hadoop versions.

25. What is a SequenceFile in Hadoop?

Extensively used in MapReduce I/O formats, SequenceFile is a flat file containing binary key–value pairs. The map outputs are stored as SequenceFile internally. It provides Reader, Writer, and Sorter classes. The three SequenceFile formats are as follows:

  1. Uncompressed key–value records
  2. Record compressed key–value records—only ‘values’ are compressed here
  3. Block compressed key–value records—both keys and values are collected in ‘blocks’ separately and compressed. The size of the ‘block’ is configurable

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26. What do you mean by WAL in HBase?

WAL is otherwise referred to as Write Ahead Log. This file is attached to each Region Server present inside the distributed environment. It stores the new data which is yet to be kept in permanent storage. WAL is often used to recover data sets in case of any failure.

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27. Explain the architecture of YARN and how it allocates various resources to applications.

There is an application, API, or client that communicates with the ResourceManager which then deals with allocating resources in the cluster. It has an awareness of the resources present will each node manager. There are two internal components of the ResourceManager: Application Manager and Scheduler. The scheduler is responsible for allocating resources to the numerous applications running in parallel based on their requirements. However, the scheduler does not track the application status.

The Application Managers accepts the submission of jobs and manages and reboots the application masters if there is a failure. It manages the applications’ demand for resources and communicates with the scheduler to get the resources needed. It interacts with the NodeManager to manage and execute the tasks that monitor the jobs running. Moreover, it also monitors the resources utilized by each container.

A container consists of a set of resources, including CPU, RAM, and network bandwidth. It allows the applications to use a predefined number of resources.

The ResourceManager sends a request to the NodeManager to keep a few resources to process as soon as there is a job submission. Later, the NodeManager assigns an available container to carry out the processing. The ResourceManager then starts the application master to deal with the execution and it runs in one of the given containers. The rest of the containers available are used for the execution process. This is the overall process of how YARN allocates resources to applications via its architecture.

28. What is the difference between Sqoop and Flume?

Following are the various differences between Sqoop and Flume:

Sqoop Flume
It works with NoSQL databases and RDBMS for importing and exporting data. It works with streaming data which is regularly generated in the Hadoop environment.
In Sqoop, loading data is not event-driven. In Flume, loading data is event-driven.
It deals with data sources that are structured and Sqoop connectors help in extracting data from them. It extracts streaming data from application servers or web servers.
It takes data from RDBMS, imports it into HDFS, and exports it back to RDBMS. Data from multiple sources flows into HDFS.

29. What is the role of a JobTracker in Hadoop?

A JobTracker’s primary function is resource management (managing the TaskTrackers), tracking resource availability, and task life cycle management (tracking the tasks’ progress and fault tolerance).

  • It is a process that runs on a separate node, often not on a DataNode.
  • The JobTracker communicates with the NameNode to identify data location.
  • It finds the best TaskTracker nodes to execute the tasks on the given nodes.
  • It monitors individual TaskTrackers and submits the overall job back to the client.
  • It tracks the execution of MapReduce workloads local to the slave node.

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30. What are the components of the architecture of Hive?

  • User Interface: It requests for the execute interface for the driver and also builds a session for this query. Further, the query is sent to the compiler in order to create an execution plan for the same.
  • Metastore: It stores the metadata and transfers it to the compile to execute a query.
  • Compiler: It creates the execution plan. The compiler consists of a DAG of stages wherein each stage can either be a map, a metadata operation, or reduces an operation or a job on HDFS.
  • Execution Engine: This engine bridges the gap between Hadoop and Hive and helps in processing the query. It communicates with the metastore bidirectionally in order to perform various tasks.

31. Is it possible to import or export tables in HBase?

Yes, you can import and export tables between HBase clusters using the commands listed below:

For export:

hbase org.apache.hadoop.hbase.mapreduce.Export “table name” “target export location”

For import:

create ‘emp_table_import’, {NAME => ‘myfam’, VERSIONS => 10}
hbase org.apache.hadoop.hbase.mapreduce.Import “table name” “target import location”

32. Why does Hive not store metadata in HDFS?

Hive stores the data of HDFS and the metadata is stored in the RDBMS or it is locally stored. HDFS does not store this metadata because the read/write operations in HDFS take a lot of time. This is why Hive uses RDBMS to store this metadata in the megastore rather than HDFS. This makes the process faster and enables you to achieve low latency.

33. What are the significant components in the execution environment of Pig?

The main components of a Pig execution environment are as follows:

  • Pig Scripts: They are written in Pig with the help of UDFs and built-in operators after which they are sent to the execution environment.
  • Parser: It checks the script syntax and completes type checking. Parser’s output is a Directed Acyclic Graph (DAG).
  • Optimizer: It conducts optimization with operations like transform, merges, and more, to minimize the data in the pipeline.
  • Compiler: The compiler automatically converts the code that is optimized into a MapReduce job.
  • Execution Engine: The MapReduce jobs are sent to these engines in order to get the required output.

34. What are the components of HBase?

The major components of HBase are as follows:

  • Region Server: It consists of HBase tables which are horizontally divided into Regions as per their key values. Moreover, it runs on every single node to estimate the region size. All region servers are worker nodes that work on various client requests, like update, delete, read, and write.
  • HMaster: HMaster assigns regions to the respective region servers for the purpose of load balancing. Further, it monitors the Hadoop cluster and can be used by clients if and when a schema or a metadata operation needs to be changed.
  • ZooKeeper: ZooKeeper offers a distributed service to coordinate and manage the state of the server in a cluster. It also checks which servers are available and sends notifications of server failure. Region servers inform Zookeeper their status to indicate whether they are ready for the read and write operations.

35. What is the command used to open a connection in HBase?

The code mentioned below can be used to open a connection in HBase:

Configuration myConf = HBaseConfiguration.create();
HTableInterface usersTable = new HTable(myConf, “users”);

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36. What is the use of RecordReader in Hadoop?

Though InputSplit defines a slice of work, it does not describe how to access it. Here is where the RecordReader class comes into the picture, which takes the byte-oriented data from its source and converts it into record-oriented key–value pairs such that it is fit for the Mapper task to read it. Meanwhile, InputFormat defines this Hadoop RecordReader instance.

37. How does Sqoop import or export data between HDFS and RDBMS?

The steps followed by Sqoop to import and export data between HDFS and RDBMS using its architecture are listed below:

  • Search the database to collect metadata.
  • Sqoop splits the input dataset and makes use of respective map jobs to push these splits to HDFS.
  • Search the database to collect metadata.
  • Sqoop splits the input dataset and makes use of respective map jobs to push these splits to RDBMS. Sqoop exports back the Hadoop files to the RDBMS tables.

38. What is Speculative Execution in Hadoop?

One limitation of Hadoop is that by distributing the tasks on several nodes, there are chances that few slow nodes limit the rest of the program. There are various reasons for the tasks to be slow, which are sometimes not easy to detect. Instead of identifying and fixing the slow-running tasks, Hadoop tries to detect when the task runs slower than expected and then launches other equivalent tasks as backup. This backup mechanism in Hadoop is speculative execution.

It creates a duplicate task on another disk. The same input can be processed multiple times in parallel. When most tasks in a job comes to completion, the speculative execution mechanism schedules duplicate copies of the remaining tasks (which are slower) across the nodes that are free currently. When these tasks are finished, it is intimated to the JobTracker. If other copies are executing speculatively, Hadoop notifies the TaskTrackers to quit those tasks and reject their output.

Speculative execution is by default true in Hadoop. To disable it, we can set mapred.map.tasks.speculative.execution and mapred.reduce.tasks.speculative.execution
JobConf options to false.

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39. What is Apache Oozie?

Oozie is nothing but a scheduler that helps to schedule jobs in Hadoop and bundles them as a single logical work. Oozie jobs can largely be divided into the following two categories:

  • Oozie Workflow: These jobs are a set of sequential actions that need to be executed.
  • Oozie Coordinator: These jobs are triggered as and when there is data availability for them until which, it rests.

40. What happens if you try to run a Hadoop job with an output directory that is already present?

It will throw an exception saying that the output file directory already exists.

To run the MapReduce job, you need to ensure that the output directory does not exist in the HDFS.

To delete the directory before running the job, we can use shell:

Hadoop fs –rmr /path/to/your/output/

Or the Java API:

FileSystem.getlocal(conf).delete(outputDir, true);

41. How can you debug Hadoop code?

First, we should check the list of MapReduce jobs currently running. Next, we need to see that there are no orphaned jobs running; if yes, we need to determine the location of RM logs.

  1. Run:
    ps –ef | grep –I ResourceManager

    Then, look for the log directory in the displayed result. We have to find out the job ID from the displayed list and check if there is any error message associated with that job.

  2. On the basis of RM logs, we need to identify the worker node that was involved in the execution of the task.
  3. Now, we will login to that node and run the below code:
    ps –ef | grep –iNodeManager
  4. Then, we will examine the Node Manager log. The majority of errors come from the user-level logs for each MapReduce job.

42. How to configure Replication Factor in HDFS?

The hdfs-site.xml file is used to configure HDFS. Changing the dfs.replication property in hdfs-site.xml will change the default replication for all the files placed in HDFS.
We can also modify the replication factor on a per-file basis using the below:

Hadoop FS Shell:[[email protected] ~]$ hadoopfs –setrep –w 3 /my/fileConversely,

We can also change the replication factor of all the files under a directory.

[[email protected] ~]$ hadoopfs –setrep –w 3 -R /my/dir

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43. How to compress a Mapper output not touching Reducer output?

To achieve this compression, we should set:

conf.set("mapreduce.map.output.compress", true)
conf.set("mapreduce.output.fileoutputformat.compress", false)

44. What is the difference between Map-side Join and Reduce-side Join?

Map-side Join at Map side is performed when data reaches the Map. We need a strict structure for defining Map-side Join.

Map Side Join

On the other hand, Reduce-side Join (Repartitioned Join) is simpler than Map-side Join since here the input datasets need not be structured. However, it is less efficient as it will have to go through sort and shuffle phases, coming with network overheads.

45. How can you transfer data from Hive to HDFS?

By writing the query:

hive> insert overwrite directory '/' select * from emp;

We can write our query for the data we want to import from Hive to HDFS. The output we receive will be stored in part files in the specified HDFS path.

46. Which companies use Hadoop?

Yahoo! (it is the biggest contributor to the creation of Hadoop; its search engine uses Hadoop); Facebook (developed Hive for analysis); Amazon; Netflix; Adobe; eBay; Spotify; Twitter; and Adobe.

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24 thoughts on “Top Hadoop Interview Questions and Answers”

  1. Good Questions for interviews . It helped me to understand many of the concepts which i was lacking while reading online . Thanks

  2. Very Good IQA. I am going to start facing interviews soon for hadoop development. Now I can say I am prepared for my interview round.

  3. Thanks Intellipaat for providing these questions and I see most of the questions present here was asked to me at TCS interview which I faced last week and which made me answer them to the point. Keep doing the good work and help the community to grow !

  4. Thanks to author for sharing such a good collection of hadoop interview questions. I want a suggestion that from where I have to prepare for CCDH. I learned Hadoop recently and now I want to take Cloudera Certification.

  5. Excellent work….
    I think 70 % of questions are being asked most commonly in interviews..
    great to get with the right answers.

  6. Hi ,
    Nice interview question.
    can you tell me what is the syllabus of hadoop certification
    and what i should do for certification .
    please suggest me.

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