Top Hadoop Big Data Interview Questions and Answers for Fresher

Hadoop Big Data Interview Question and Answer
Top Hadoop Big Data Analytics Interview
Questions and Answers for Fresher and
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Hadoop Big Data Interview Question & Answers
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Q1) What are real-time industry applications of Hadoop?
Ans: 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. Some of the instances where Hadoop is used:
1. Managing traffic on streets.
2. Streaming processing.
3. Content Management and Archiving Emails.
4. Processing Rat Brain Neuronal Signals using a Hadoop Computing Cluster.
5. Fraud detection and Prevention.
6. Advertisements Targeting Platforms are using Hadoop to capture and analyze click
stream, transaction, video and social media data.
7. Managing content, posts, images and videos on social media platforms.
8. Analyzing customer data in real-time for improving business performance.
9. Public sector fields such as intelligence, defense, cyber security and scientific research.

Q2) How is Hadoop different from other parallel computing systems?
Ans: Hadoop is a distributed file system, which lets you store and handle massive amount
of data on a cloud of machines, handling data redundancy. Go through this HDFS content
to know how the distributed file system works. The primary benefit is that since data is
stored in several nodes, it is better to process it in distributed manner. Each node can
process the data stored on it instead of spending time in moving it over the network.
On the contrary, in Relational database computing system, you can query data in real-
time, but it is not efficient to store data in tables, records and columns when the data is
huge.
Learn about Oracle DBA now.
Hadoop also provides a scheme to build a Column Database with Hadoop HBase, for
runtime queries on rows.

Q3) What all modes Hadoop can be run in?
Ans: Hadoop can run in three modes:
1. Standalone Mode: Default mode of Hadoop, it uses local file stystem for input and
output operations. This mode is mainly used for 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, hdfs-site.xml files. Much faster when
compared to other modes.
2. 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 node are the same.
3. Fully Distributed Mode (Multiple Cluster Node): 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.

Q4) What is distributed cache and what are its benefits?
Ans: Distributed Cache, in Hadoop, is a service by MapReduce framework to cache files
when needed. Learn more in this MapReduce Tutorial now. Once a file is cached for a
specific job, hadoop will make it available on each data node 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.
Benefits of using distributed cache are:
1. It distributes simple, read only text/data files and/or complex types like jars,
archives and others. These archives are then un-archived at the slave node.
2. Distributed cache tracks the modification timestamps of cache files, which
notifies that the files should not be modified until a job is executing currently.

Q5) Explain the difference between NameNode, Checkpoint NameNode and
BackupNode.
Ans:
1. NameNode is the core of HDFS that manages the metadata – the information of what
file maps to what block locations and what blocks are stored on what 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.
2. Checkpoint NameNode has the same directory structure as NameNode, and creates
checkpoints for namespace at regular intervals by downloading the fsimage and edits
file and margining them within the local directory. The new image after merging is
then uploaded to NameNode.
3. Backup Node provides similar functionality as Checkpoint, enforcing synchronization
with NameNode. It maintains an up-to-date in-memory copy of 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.

Q6) What are the most common Input Formats in Hadoop?
Ans: There are three most common input formats in Hadoop:
1. Text Input Format: Default input format in Hadoop.
2. Key Value Input Format: used for plain text files where the files are broken into lines
3. Sequence File Input Format: used for reading files in sequence

Q7) Define DataNode and how does NameNode tackle DataNode failures?
Ans: 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 noit receive a message from datanode for 10 minutes, it considers it to
be dead or out of place, and starts replication of blocks that were hosted on that data
node such that they are hosted on some other data node.A BlockReport contains list of all
blocks on a DataNode. Now, the system starts to replicate what were stored in dead
DataNode.
The NameNode manages the replication of data blocksfrom one DataNode to other. In
this process, the replication data transfers directly between DataNode such that the data
never passes the NameNode.

Q8) What are the core methods of a Reducer?
Ans: The three core methods of a Reducer are:
1. setup(): this method is used for configuring various parameters like input data size,
distributed cache.
public void setup (context)
2. reduce(): heart of the reducer always called once per key with the associated reduced
task
public void reduce(Key, Value, context)
3. cleanup(): this method is called to clean temporary files, only once at the end of the
task
public void cleanup (context)

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