Define Big Data and explain the Vs of Big Data.
This is one of the most introductory yet important Big Data interview questions. The answer to this is quite straightforward:
Big Data can be defined as a collection of complex unstructured or semi-structured data sets, which have the potential to deliver actionable insights.
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
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How is Hadoop related to Big Data?
When we talk about Big Data, we talk about Hadoop. So, this is another Big Data interview question that you will definitely face in an interview.
Hadoop is an open-source framework for storing, processing, and analyzing complex unstructured data sets for deriving insights and intelligence.
Define HDFS and YARN, and talk about their respective components.
Now that we’re in the zone of Hadoop, the next Big Data interview question you might face will revolve around the same.
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.
The two main components of YARN are –
ResourceManager – Responsible for allocating resources to respective NodeManagers based on the needs.
NodeManager – Executes tasks on every DataNode.
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What do you mean by commodity hardware?
This is yet another Big Data interview question you’re most likely to come across in any interview you sit for.
Commodity Hardware refers to the minimal hardware resources needed to run the Apache Hadoop framework. Any hardware that supports Hadoop’s minimum requirements is known as ‘Commodity Hardware.’
Define and describe the term FSCK.
FSCK stands for Filesystem Check. It is a command used to run a Hadoop summary report that describes the state of HDFS. It only checks for errors and does not correct them. This command can be executed on either the whole system or a subset of files.
What is the purpose of the JPS command in Hadoop?
The JPS command is used for testing the working of all the Hadoop daemons. It specifically tests daemons like NameNode, DataNode, ResourceManager, NodeManager and more.
(In any Big Data interview, you’re likely to find one question on JPS and its importance.)
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Name the different commands for starting up and shutting down Hadoop Daemons.
This is one of the most important Big Data interview questions to help the interviewer gauge your knowledge of commands.
To start all the daemons:
To shut down all the daemons:
Why do we need Hadoop for Big Data Analytics?
This Hadoop interview questions test your awareness regarding the practical aspects of Big Data and Analytics.
In most cases, Hadoop helps in exploring and analyzing large and unstructured data sets. Hadoop offers storage, processing and data collection capabilities that help in analytics.
Explain the different features of Hadoop.
Listed in many Big Data Interview Questions and Answers, the best answer to this is –
Open-Source – Hadoop is an open-sourced platform. It allows the code to be rewritten or modified according to user and analytics requirements.
Scalability – Hadoop supports the addition of hardware resources to the new nodes.
Data Recovery – Hadoop follows replication which allows the recovery of data in the case of any failure.
Data Locality – This means that Hadoop moves the computation to the data and not the other way round. This way, the whole process speeds up.
Define the Port Numbers for NameNode, Task Tracker and Job Tracker.
NameNode – Port 50070
Task Tracker – Port 50060
Job Tracker – Port 50030
What do you mean by indexing in HDFS?
HDFS indexes data blocks based on their sizes. The end of a data block points to the address of where the next chunk of data blocks get stored. The DataNodes store the blocks of data while NameNode stores these data blocks.
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What are Edge Nodes in Hadoop?
Edge nodes refer to the gateway nodes which act as an interface between Hadoop cluster and the external network. These nodes run client applications and cluster management tools and are used as staging areas as well. Enterprise-class storage capabilities are required for Edge Nodes, and a single edge node usually suffices for multiple Hadoop clusters.
What are some of the data management tools used with Edge Nodes in Hadoop?
This Big Data interview question aims to test your awareness regarding various tools and frameworks.
Oozie, Ambari, Pig and Flume are the most common data management tools that work with Edge Nodes in Hadoop.
Explain 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.
Talk about the different tombstone markers used for deletion purposes in HBase.
This Big Data interview question dives into your knowledge of HBase and its working.
There are three main tombstone markers used for deletion in HBase. They are-
Family Delete Marker – For marking all the columns of a column family.
Version Delete Marker – For marking a single version of a single column.
Column Delete Marker – For marking all the versions of a single column.