Storing and processing big data has remained the biggest
challenge until today since the beginning of its journey. It is
important to be able to compute datasets to generate solutions for
businesses. But sometimes, it becomes really challenging to produce
accurate results due to the outliers, scarcity of sources, Volume,
and inconsistency. But there is no value of big data if you can not use it or
extract meaningful information. The below mentioned Hadoop
Interview Questions would help you to get a solid foundation and
face interviews as well. [1]
Hadoop is a great solution or can be seen as a data warehouse
that can store and process big data efficiently. It helps to bring
out insights and knowledge easily. Besides, data modeling, data
analytics, data scalability, and data computations capabilities
have made Hadoop so popular among companies and individuals. So it
is important to go through these Hadoop Interview Questions if you
want to establish your career around cloud computing.
Hadoop is developed by Apache Software Foundation. It started
the journey on April 1, 2006, and licensed under Apache License
2.0. It is a framework that allows people to work with massive
amounts of data. Besides, it uses the MapReduce algorithm and
ensures high availability, which is the most exclusive feature any
business can offer. You should make sure that you understand all
the basic concepts of cloud computing. Otherwise, you will face
trouble while going through the following Hadoop interview
questions.
Hadoop Interview Questions and
Answers
It is important to go through these Hadoop Interview Questions
in-depth if you are a candidate and want to start a job in the
cloud computing industry. These
questions and answers covered throughout this article will
definitely help you to be on the right track. [2]
As most companies are running businesses based on the decisions
derived from analyzing big data, more skillful people are required
to produce better results. It can improve an individual’s
efficiency and thus contribute to generating sustainable results.
As a collection of open-source software utilities, it can process
huge datasets across clusters of computers. This article highlights
all the basics and advanced topics of Hadoop. Besides, it will save
a lot of time for you and prepare yourself well enough for the
interviews.
Q-1. What is Hadoop?
As people
of today’s day and age, we know the complexity of analyzing big
data and how difficult it can be to compute a huge amount of data
for producing business solutions. Apache Hadoop was introduced in
2006 that helps to store, manage, and process big data. It is a
framework and uses the MapReduce programming model to distribute
storage and process dataset.
As a collection of open-source software utilities, it turned out
to be a great system that helps in making data-driven decisions and
manage businesses effectively and efficiently. It was developed by
Apache Software Foundation and licensed under Apache License
2.0.
Cluster Rebalancing: Automatically
free up the space of data nodes approaching a certain threshold and
rebalances data.
Accessibility: There are so many ways
to access Hadoop from different applications. Besides, the web
interface of Hadoop also allows you to browse HDFS files using any
HTTP browser.
Re-replication: In case of a missing
block, NameNode recognizes it as a dead block, which is then
re-replicated from another node. It protects the hard disk from
failure and decreases the possibility of data loss.
Q-2. Mention the names of the
foremost components of Hadoop.
Hadoop has enabled us to run applications on a system where
thousands of hardware nodes are incorporated. Besides, Hadoop can
also be used for transferring data rapidly. There are three main
components of the Apache Hadoop Ecosystem: HDFS, MapReduce, and
YARN.
HDFS: Used for storing data and all
the applications.
MapReduce: Used for processing of stored
data and driving solutions through computation.
YARN: Manages the resources that are
present in Hadoop.
Interviewers love to ask these Hadoop admin interview questions
because of the amount of information they can cover and judge the
candidate’s capability very well.
Q-3. What do you understand by
HDFS?
HDFS
is one of the main components of the Hadoop framework. It provides
storage for datasets and allows us to run other applications as
well. The two major parts of HDFS are NameNode and DataNode.
NameNode: It can be referred to as the
master node, which contains the metadata information such as block
location, factors of replication, and so on for each data block
stored in Hadoop’s distributed environment.
DataNode: It is maintained by NameNode
and works as a slave node to store data in HDFS.
This is one of the most important frequently asked Hadoop
Interview Questions. You can easily expect this question on your
coming interviews.
Q-4. What is YARN?
YARN
processes the resources available in the Hadoop environment and
provides an environment of execution for the applications.
ResourceManager and NodeManager are the two major components of
YARN.
ResourceManager: It delivers the
resources to the application according to the requirement. Besides,
it is responsible for receiving the processing requests and
forwarding them to the associated NodeManager.
NodeManager: After receiving the
resources from ResourceManager, NodeManager starts processing. It
is installed on every data node and performs the execution task as
well.
Q-5. Can you mention the principal
differences between the relational database and HDFS?
Differences between the relational database and HDFS can be
described in terms of Data types, processing, schema, read or write
speed, cost, and best-fit use case.
Data types: Relational databases
depend on the structures data while the schema can also be known.
On the other hand, structured, unstructured, or semi-structured
data is allowed to store in HDFS.
Processing: RDBMS does not have the
processing ability, while HDFS can process datasets to execute in
the distributed clustered network.
Schema: Schema validation is done even
before the data is loaded when it comes to RDBMS, as it follows
schema on write fashion. But HDFS follows a schema on reading
policy for validating data.
Read/Write Speed: As data is already
known, reading is fast in the relational database. On the contrary,
HDFS can write fast due to the absence of data validation during
the writing operation.
Cost: You will need to pay for using a
relational database as it is a licensed product. But Hadoop is an
open-source framework so it will not cost even a penny.
Best-fit Use Case: RDBMS is suitable
to use for Online Transactional Processing while Hadoop can be used
for many purposes, and it can also enhance the functionalities of
an OLAP system like data discovery or data analytics.
Q-6. Explain the role of various
Hadoop daemons in a Hadoop cluster.
Daemons can be classified into two categories. They are HDFS
daemons and YARN daemons. While NameNode, DataNode, and Secondary
Namenode are part of HDFS, YARN daemons include ResorceManager and
NodeManager alongside the JobHistoryServer, which is responsible
for keeping important information MapReduce after the master
application is terminated.
Q-7. How can we discriminate HDFS
and NAS?
The differences between HDFS and NAS asked in this Hadoop
related question can be explained as follows:
- NAS is a file-level server that is used to provide access to a
heterogeneous group through a computer network. But when it comes
to HDFS, it utilizes commodity hardware for storing purpose. - If you store data in HDFS, it becomes available to all the
machines connected to the distributed cluster while in Network
Attached Storage, data remains visible only to the dedicated
computers. - NAS can not process MapReduce due to the absence of
communication between data blocks and computation, while HDFS is
known for its capability of working with the MapReduce
paradigm. - Commodity hardware is used in HDFS to decrease the cost while
NAS uses high-end devices, and they are expensive.
Q-8. How does Hadoop 2 function
better than Hadoop 1?
NameNode can fail anytime in Hadoop 1, and
there is no backup to cover the failure. But in Hadoop 2, in case
the active “NameNode” fails, passive “NameNode” can take charge,
which shares all the common resources so that the high availability
can be achieved easily in Hadoop.
There is a central manager in YARN, which allows us to run
multiple applications in Hadoop. Hadoop 2 utilizes the power of the
MRV2 application, which can operate the MapReduce framework on top
of YARN. But other tools can not use YARN for data processing when
it comes to Hadoop 1.
Q-9. What can be referred to as
active and passive “NameNodes”?
Hadoop 2 has introduced passive NameNode, which is a great
development that increases availability to a great extent. Active
NameNode is primarily used in the cluster to work and run. But in
any unexpected situation, if active NameNode fails, disruption can
occur.
But in these circumstances, passive NameNode plays an important
role that contains the same resources as active NameNode. It can
replace the active NameNode when required so the system can never
fail.
Q-10. Why adding or removing nodes
is done frequently in the Hadoop cluster?
Hadoop framework is scalable and popular for its capability of
utilizing the commodity hardware. DataNode crashing is a common
phenomenon in the Hadoop cluster. And again, the system
automatically scales according to the Volume of data. So, it can be
easily understood that commissioning and decommissioning DataNodes
is done rapidly, and it is one of the most striking features of
Hadoop.
Q-11. What happens when HDFS
receives two different requests for the same
resource?
Although HDFS can handle several clients at a time, it supports
exclusive writes only. That means if a client asks to get access to
an existing resource, HDFS responds by granting permission. As a
result, the client can open the file for writing. But when another
client asks for the same file, HDFS notices the file is already
leased to another client. So, it automatically rejects the request
and let the client know.
Q-12. What does NameNode do when
DataNode fails?
If the DataNode is working properly, it can transmit a signal
from each DataNode in the cluster to the NameNode periodically and
known as the heartbeat. When no heartbeat message is transmitted
from the DataNode, the system takes some time before marking it as
dead. NameNode gets this message from the block report where all
the blocks of a DataNode are stored.
If NameNode identifies any dead DataNode, it performs an
important responsibility to recover from the failure. Using the
replicas that have been created earlier, NameNode replicates the
dead node to another DataNode.
Q-13. What are the procedures
needed to be taken when a NameNode fails?
When NameNode is down, one should perform the following tasks to
turn the Hadoop cluster up and run again:
- A new NameNode should be created. In this case, you can use the
file system replica and start a new node. - After creating a new node, we will need to let clients and
DataNodes know about this new NameNode so that they can acknowledge
it. - Once you complete the last loading checkpoint known as FsImage,
the new NameNode is ready to serve the clients. But to get going,
NameNode must receive enough block reports coming from the
DataNodes. - Do routine maintenance as if NameNode is down in a complex
Hadoop cluster, it may take a lot of effort and time to
recover.
Q-14. What is the role of
Checkpointing in the Hadoop environment?
The process of editing log of a file system or FsImage and
compacting them into a new FsImage in a Hadoop framework is known
as Checkpointing. FsImage can hold the last in-memory, which is
then transferred to NameNode to reduce the necessity of replaying a
log again.
As a result, the system becomes more efficient, and the required
startup time of NameNode can also be reduced. To conclude, it
should be noted that this process is completed by the Secondary
NameNode.
Q-15. Mention the feature, which
makes the HDFS fraud tolerant.
This Hadoop related question asks whether HDFS is fraud tolerant
or not. The answer is yes, HDFS is fraud tolerant. When data is
stored, NameNode can replicate data after storing it to several
DataNodes. It creates 3 instances of the file automatically as the
default value. However, you can always change the number of
replication according to your requirements.
When a DataNode is labeled as dead, NameNode takes information
from the replicas and transfers it to a new DataNode. So, the data
becomes available again in no time, and this process of replication
provides fault tolerance in the Hadoop Distributed File System[3].
Q-16. Can NameNode and
DataNodefunction like commodity hardware?
If you want
to answer these Hadoop admin interview questions smartly, then you
can consider DataNode as like personal computers or laptops as it
can store data. These DataNodes are required in a large number to
support the Hadoop Architecture, and they are like commodity
hardware.
Again, NameNode contains metadata about all data blocks in HDFS,
and it takes a lot of computational power. It can be compared to
random access memory or RAM as a High-End Device, and good memory
speed is required to perform these activities.
Q-17. Where should we use HDFS?
Justify your answer.
When we need to deal with a large dataset that is incorporated
or compacted into a single file, we should use HDFS. It is more
suitable to work with a single file and is not much effective when
the data is spread in small quantities across multiple files.
NameNode works like a RAM in the Hadoop distribution system and
contains metadata. If we use HDFS to deal with too many files, then
we will be storing too many metadata. So NameNode or RAM will have
to face a great challenge to store metadata as each metadata may
take minimum storage of 150 bytes.
Q-18. What should we do to explain
“block” in HDFS?
Do you know the default block size of Hadoop 1 and Hadoop
2?
Blocks can be referred to as continuous memory on the hard
drive. It is used to store data, and as we know, HDFS stores each
data as a block before distributing it throughout the cluster. In
the Hadoop framework, files are broken down into blocks and then
stored as independent units.
- Default block size in Hadoop 1: 64 MB
- Default block size in Hadoop 2: 128 MB
Besides, you can also configure the block size using the
dfs.block.size parameter. If you want to know the size
of a block in HDFS, use the hdfs-site.xml file.
Q-19. When do we need to use the
‘jps’ command?
Namenode, Datanode, resourcemanager, nodemanager, and so on are
the available daemons in the Hadoop environment. If you want to
have a look at all the currently running daemons on your machine,
use ‘jps’ command to see the list. It is one of the frequently used
commands in HDFS.
Interviewers love to ask command related Hadoop developer
interview questions, so try to understand the usage of frequently
used commands in Hadoop.
Q-20. What can be referred to as
the five V’s of Big Data?
Velocity,
Volume, variety, veracity, and value are the five V’s of big data.
It is one of the most important Hadoop admin interview questions.
We are going to explain the five V’s in brief.
Velocity: Big data deals with the
ever-growing dataset that can be huge and complicated to compute.
Velocity refers to the increasing data rate.
Volume: Represents the Volume of data
that grows at an exponential rate. Usually, Volume is measured in
Petabytes and Exabytes.
Variety: It refers to the wide range
of variety in data types such as videos, audios, CSV, images, text,
and so on.
Veracity: Data often becomes
incomplete and becomes challenging to produce data-driven results.
Inaccuracy and inconsistency are common phenomenons and known as
veracity.
Value: Big data can add value to any
organization by providing advantages in making data-driven
decisions. Big data is not an asset unless the value is extracted
out of it.
Q-21. What do you mean by “Rack
Awareness” in Hadoop?
This Hadoop related question focuses on Rack Awareness, which
is an algorithm that defines the placement of the replicas. It is
responsible for minimizing the traffic between DataNode and
NameNode based on the replica placement policy. If you do not
change anything, replication will be occurred up to 3times.
Usually, it places two replicas in the same rack while another
replica is placed on a different rack.
Q-22. Describe the role of
“Speculative Execution” in Hadoop?
Speculative Execution is responsible for executing a
task redundantly when a slow running task is identified. It creates
another instance of the same job on a different DataNode. But which
task finishes first is accepted automatically while another case is
destroyed. This Hadoop related question is important for any cloud
computing interview.
Q-23. What should we do to perform
the restart operation for “NameNode” in the Hadoop
cluster?
Two distinct methods can enable you to restart the NameNode or
the daemons associated with the Hadoop framework. To choose the
most suitable process to restart “NameNode” have a look at your
requirements.
If you want to stop the NameNode only /sbin namenode command can be used. To
/hadoop-daemon.sh stop
start the NameNode again use /sbin/hadoop-daemon.sh namenode command.
start
Again, /sbin/stop-all.sh command is useful when it
comes to stopping all the daemons in the cluster while
./sbin/start-all.sh command can be used for starting all the
daemons in the Hadoop framework.
Q-24. Differentiate “HDFS Block”
and an “Input Split”.
It is one of the most frequently asked Hadoop Interview
Questions. There is a significant difference between HDFS Block and
Input Split. HDFS Block divides data into blocks using MapReduce
processing before assigning it to a particular mapper function.
In other words, HDFS Block can be viewed as the physical
division of data, while Input Split is responsible for the logical
division in the Hadoop environment.
Q-25. Describe the three
modes that Hadoop can run.
The three modes which Hadoop framework can run are described
below:
Standalone mode: In this mode,
NameNode, DataNode, ResourceManager, and NodeManager function as a
single Java process that utilizes a local filesystem, and no
configuration is required.
Pseudo-distributed mode: Master and
slave services are executed on a single compute node in this mode.
This phenomenon is also known as the running mode in
HDFS.
Fully distributed mode: Unlike the
Pseudo-distributed mode, master and slave services are executed on
fully distributed nodes that are separate from each other.
Q-26. What is MapReduce? Can you
mention its syntax?
MapReduce is an integral part of the Hadoop file distributed
system. Interviewers love to ask this kind of Hadoop developer
interview questions to challenge the candidates.
As a programming model or process MapReduce can handle big data
over a cluster of computers. It uses parallel programming for
computing. If you want to run a MapReduce program, you can use
“hadoop_jar_file.jar /input_path /output_path” like
syntax.
Q-27. What are the components that
are required to be configured for a MapReduce
program?
This Hadoop related question asks about the parameters to run a
MapReduce program components needed to be configured mentioned
below:
- Mention the input locations of jobs in HDFS.
- Define the locations where the output will be saved in
HDFS. - Mention the input type of data.
- Declare the output type of data.
- The class that contains the required map function.
- The class that contains the reduce function.
- Look for a JAR file to get the mapper reducer, and driver
classes.
Q-28. Is it possible to perform
the “aggregation” operation in the mapper?
It is a tricky Hadoop related question in the list of Hadoop
Interview Questions. There can be several reasons which are stated
as follows:
- We are not allowed to perform sorting in the mapper function as
it is meant to be performed only on the reducer side. So we can not
perform aggregation in mapper as it is not possible without
sorting. - Another reason can be, If mappers run on different machines,
then it is not possible to perform aggregation. Mapper functions
may not be free, but it is important to collect them in the map
phase. - Building communication between the mapper functions is crucial.
But as they are running on different machines, it will take High
bandwidth. - Network bottlenecks can be considered as another common result
if we want to perform aggregation.
Q-29. How does “RecordReader”
perform in Hadoop?
InputSplit can not describe how to access work as it is only
able to define tasks. Thanks to the “RecordReader” class as it
contains the source of the data, which is then converted into a
pair (key, value). “Mapper” task can easily identify the pairs
while you should also note that the Input Format can declare the
“RecordReader” instance.
Q-30. Why does “Distributed Cache”
play an important role in a “MapReduce Framework”?
Distributed
cache plays an important role in the Hadoop Architecture, and you
should focus on similar Hadoop Interview Questions. This unique
feature of the MapReduce framework allows you to cache files when
required. When you cache any file, it becomes available on every
data node. It will be added to the currently running
mappers/reducers and easily accessible.
Q-31. What is the communication
process between reducers?
In this list of Hadoop developer interview questions, this
question should be highlighted separately. Interviewers just love
to ask this question, and you can expect this anytime. The answer
is reducers are not allowed to communicate. They are run by the
MapReduce programming model in isolation.
Q-32. How does the “MapReduce
Partitioner” play a role in Hadoop?
“MapReduce Partitioner” is responsible for sending all single
critical values to the same “reducer.” Sends the output of map
distribution over “reducers so that it can identify the “reducer”
responsible for a specific key. So it can transmit the mapper
output to that “reducer.”
Q-33. Mention the process of
writing a custom partitioner?
If you want to write a custom partitioner, then you should
follow the following steps:
- At first, you will need to create a new class that can extend
the Partitioner Class. - Secondly, use the getPartition override method in the wrapper
so that it can run MapReduce. - Set Partitioner for adding the custom Partitioner to a job
should be used at this point. However, you can also add a custom
partitioner as a config file.
Q-34. What do you mean by a
“Combiner”?
A “Combiner” can be compared to a mini reducer that can perform
the “reduce” task locally. It receives the input from the “mapper”
on a particular “node” and transmits it to the “reducer”. It
reduces the volume of data required to send to the “reducer” and
improves the efficiency of MapReduce. This Hadoop related question
is really important for any cloud computing interview.
Q-35. What is
“SequenceFileInputFormat”?
It is an input format and suitable for performing the reading
operation within sequence files. This binary file format can
compress and optimizes the data so that it can be transferred from
the outputs of one “MapReduce” job to the input of another
“MapReduce” job.
It also helps in generating sequential files as the output of
MapReduce tasks. The intermediate representation is another
advantage that makes data suitable for sending from one task to
another.
Q-36. What do you mean by
shuffling in MapReduce?
The MapReduce output is transferred to as the input of another
reducer at the time of performing the sorting operation. This
process is known as “Shuffling”. Focus on this question as the
interviewers love to ask Hadoop related questions based on
operations.
Q-37. Explain Sqoop in
Hadoop.
It is
an important tool to interchange data between RDBMS and HDFS.
That’s why Interviewers love to include “Sqoop” in the Hadoop admin
interview questions. Using Sqoop, you can export data from the
Relational database management system like MySQL or ORACLE and
import in HDFS. And it is also possible to transfer data from
Apache Hadoop to RDBMS.
Q-38. What is the role of
conf.setMapper class?
This Hadoop related question asks about Conf.setMapper class
that has several important roles to play in Hadoop clusters. It
sets the mapper class while it also contributes to mapping to jobs.
Setting up reading data and generating a key-value pair out of the
mapper is also part of its responsibilities.
Q-39. Mention the names of data
and storage components. How to declare the input formats in
Hadoop?
This Hadoop related question can be asked by the interviewers as
this covers a lot of information about data type, storage type, and
input format. There are two data components used by Hadoop, and
they are Pig and Hive, while Hadoop uses HBase components to store
data resources.
You can use any of these formats to define your input in Hadoop,
which are TextInputFormat, KeyValueInputFormat, and
SequenceFileInputFormat.
Q-40. Can you search for files
using wildcards? Mention the list of configuration files used in
Hadoop?
HDFS allows us to search for files using wildcards. You can
import the data configuration wizard in the file/folder field and
specify the path to the file to conduct a search operation in
Hadoop. The three configuration files Hadoop uses are as
follows:
- core-site.xml
- mapred-site.xml
- Hdfs-site.xml
Q-41. Mention the network
requirements for using HDFS.
To get the best
service, you should establish the fastest Ethernet connections
possible with the most capacity between the racks. Besides, the
basic network requirements to use HDFS are mentioned below:
- Password-less SSH connection
- Secure Shell (SSH) for launching server processes
Many people fail to answer this kind of basic Hadoop Interview
Questions correctly as we often ignore the basic concepts before
diving into the insights.
Q-42. How can we copy files in
HDFS? How can you differentiate Hadoop from other data processing
tools?
It is an interesting question in the list of most frequently
asked Hadoop developer interview questions. HDFS deals with big
data and intended to process for adding value. We can easily copy
files from one place to another in the Hadoop framework. We use
multiple nodes and the distcp command to share the workload while
copying files in HDFS.
There are many data processing tools available out there, but
they are not capable of handling big data and processing it for
computing. But Hadoop is designed to manage big data efficiently,
and users can increase or decrease the number of mappers according
to the Volume of data needed to be processed.
Q-43. How does Avro Serialization
operate in Hadoop?
Avro
Serialization is a process used to translate objects and data
structures into binary and textual form. It is written in JSON or
can be seen as an independent language schema. Besides, you should
also note that Avro Serialization comes with great solutions such
as AvroMapper and AvroReducer to run MapReduce programs in
Hadoop.
Q-44. What are the Hadoop
schedulers? How to keep an HDFS cluster balanced?
There are three
Hadoop schedulers. They are as follows:
- Hadoop FIFO scheduler
- Hadoop Fair Scheduler
- Hadoop Capacity Scheduler
You can not really limit a cluster from being unbalanced. But a
certain threshold can be used among data nodes to provide a
balance. Thanks to the balancer tool. It is capable of even out the
block data distribution subsequently across the cluster to maintain
the balance of the Hadoop clusters.
Q-45. What do you understand by
block scanner? How to print the topology?
Block Scanner ensures the high availability of HDFS to all the
clients. It periodically checks DataNode blocks to identify bad or
dead blocks. Then it attempts to fix the block as soon as possible
before any clients can see it.
You may not remember all the commands during your interview. And
that’s why command related Hadoop admin interview questions are
really important. If you want to see the topology, you should use
hdfs dfsadmin -point the topology command. The tree of
racks and DataNodes that are attached to the tracks will be
printed.
Q-46. Mention the site-specific
configuration files available in Hadoop?
The site-specific configuration files that are available to use
in Hadoop are as follows:
- conf/Hadoop-env.sh
- conf/yarn-site.xml
- conf/yarn-env.sh
- conf/mapred-site.xml
- conf/hdfs-site.xml
- conf/core-site.xml
These basic commands are really useful. They will not only help
you to answer Hadoop Interview Questions but also get you going if
you are a beginner in Hadoop.
Q-47. Describe the role of a
client while interacting with the NameNode?
A
series of tasks needed to be completed to establish a successful
interaction between a client and the NameNode, which are described
as follows:
- Clients can associate their applications with the HDFS API to
the NameNode so that it can copy/move/add/locate/delete any file
when required. - DataNode servers that contain data will be rendered in a
list by the NameNode when it receives successful requests. - After the NameNode replies, the client can directly interact
with the DataNode as the location is now available.
Q-48. What can be referred to as
Apache Pig?
Apache Pig is useful to create Hadoop compatible programs. It is
a high-level scripting language or can be seen as a platform made
with Pig Latin programming language. Besides, the Pig’s capability
to execute the Hadoop jobs in Apache Spark or MapReduce should also
be mentioned.
Q-49. What are the data types you
can use in Apache Pig? Mention the reasons why Pig is better than
MapReduce?
Atomic data types and
complex data types are the two types of data you can use in Apache
Pig. While the Atomic type of data deals with int, string, float,
and long, complex data type includes Bag, Map, and Tuple.
You can achieve many benefits if you choose Pig over Hadoop such
as:
- MapReduce is a low-level scripting language. On the other hand,
Apache Pig is nothing but a high-level scripting language. - It can easily complete the operations or implementations which
take complex java implementations using MapReduce in Hadoop. - Pig produces compacted code, or the length of the code is less
than Apache Hadoop, which can save development time to a great
extent.
Data operations are made easy in Pig as there are many built-in
operators available such as filters, joins, sorting, ordering, and
so on. But you will need to face a lot of troubles if you want to
perform the same operations in Hadoop.
Q-50. Mention the relational
operators that are used in “Pig Latin”?
This Hadoop developer interview question asks about various
relational operators used in “Pig Latin” that are SPLIT, LIMIT,
CROSS, COGROUP, GROUP, STORE, DISTINCT, ORDER BY, JOIN, FILTER,
FOREACH, and LOAD.
Finally, Insights
We have put our best effort to provide all the frequently asked
Hadoop Interview Questions here in this article. Hadoop has
successfully attracted developers and a considerable amount of
enterprises. It is clearly under the spotlight and can be a great
option to start a career. Again, cloud computing has already taken
the place of traditional hardware infrastructures and reshaped the
processes.
If you look at the leading organizations around the world, it is
easily noticeable that if you want to deliver better products at a
lower cost, you must incorporate cloud computing with your
business[4]. As a result, the number
of jobs in this sector has increased numerously. You can expect
these Hadoop Interview Questions in any cloud computing Interview.
Besides, these questions can also set you apart from other
interviewees and clear the fundamentals of the Apache Hadoop
framework.
References
- ^
Top 20
Best Big Data Tools and Software That You Can Use in 2020
(www.ubuntupit.com) - ^
The 25
Best Cloud Computing Companies and Platforms in 2020
(www.ubuntupit.com) - ^
Hadoop Distributed File System
(www.ibm.com) - ^
Top 20
Best Cloud Computing Examples and Uses in 2020
(www.ubuntupit.com)
Read more https://www.ubuntupit.com/frequently-asked-hadoop-interview-questions-and-answers/