Our experts providing Big Data interview questions & Answers/Faqs can develop your carrier & knowledge to find the right job in a good MNC’s, doesn’t matter what kind of company you’re hired.
1)Explain “Big Data” and what are five V’s of Big Data?
“Big data” is the term for a collection of large and complex data sets, that makes it difficult to process using relational database management tools or traditional data processing applications. It is difficult to capture, curate, store, search, share, transfer, analyze, and visualize Big data. Big Data has emerged as an opportunity for companies. Now they can successfully derive value from their data and will have a distinct advantage over their competitors with enhanced business decisions making capabilities.
“Big data” is the term for a collection of large and complex data sets, that makes it difficult to process using relational database management tools or traditional data processing applications. It is difficult to capture, curate, store, search, share, transfer, analyze, and visualize Big data. Big Data has emerged as an opportunity for companies. Now they can successfully derive value from their data and will have a distinct advantage over their competitors with enhanced business decisions making capabilities.
♣ Tip: It will be a good idea to talk about the 5Vs in such questions, whether it is asked specifically or not!
Volume: The volume represents the amount of data which is growing at an exponential rate i.e. in Petabytes and Exabytes.
Velocity: Velocity refers to the rate at which data is growing, which is very fast. Today, yesterday’s data are considered as old data. Nowadays, social media is a major contributor in the velocity of growing data.
Variety: Variety refers to the heterogeneity of data types. In another word, the data which are gathered has a variety of formats like videos, audios, csv, etc. So, these various formats represent the variety of data.
Veracity: Veracity refers to the data in doubt or uncertainty of data available due to data inconsistency and incompleteness. Data available can sometimes get messy and maybe difficult to trust. With many forms of big data, quality and accuracy are difficult to control. The volume is often the reason behind for the lack of quality and accuracy in the data.
Value: It is all well and good to have access to big data but unless we can turn it into a value it is useless. By turning it into value I mean, Is it adding to the benefits of the organizations? Is the organization working on Big Data achieving high ROI (Return On Investment)? Unless, it adds to their profits by working on Big Data, it is useless.
Velocity: Velocity refers to the rate at which data is growing, which is very fast. Today, yesterday’s data are considered as old data. Nowadays, social media is a major contributor in the velocity of growing data.
Variety: Variety refers to the heterogeneity of data types. In another word, the data which are gathered has a variety of formats like videos, audios, csv, etc. So, these various formats represent the variety of data.
Veracity: Veracity refers to the data in doubt or uncertainty of data available due to data inconsistency and incompleteness. Data available can sometimes get messy and maybe difficult to trust. With many forms of big data, quality and accuracy are difficult to control. The volume is often the reason behind for the lack of quality and accuracy in the data.
Value: It is all well and good to have access to big data but unless we can turn it into a value it is useless. By turning it into value I mean, Is it adding to the benefits of the organizations? Is the organization working on Big Data achieving high ROI (Return On Investment)? Unless, it adds to their profits by working on Big Data, it is useless.
2)What is Hadoop and its components.
When “Big Data” emerged as a problem, Apache Hadoop evolved as a solution to it. Apache Hadoop is a framework which provides us various services or tools to store and process Big Data. It helps in analyzing Big Data and making business decisions out of it, which can’t be done efficiently and effectively using traditional systems.
When “Big Data” emerged as a problem, Apache Hadoop evolved as a solution to it. Apache Hadoop is a framework which provides us various services or tools to store and process Big Data. It helps in analyzing Big Data and making business decisions out of it, which can’t be done efficiently and effectively using traditional systems.
♣ Tip: Now, while explaining Hadoop, you should also explain the main components of Hadoop, i.e.:
Storage unit– HDFS (NameNode, DataNode)
Processing framework– YARN (ResourceManager, NodeManager)
Processing framework– YARN (ResourceManager, NodeManager)
3)Name some companies that use Hadoop.?
Yahoo (One of the biggest user & more than 80% code contributor to Hadoop)
Facebook
Netflix
Amazon
Adobe
eBay
Hulu
Spotify
Rubikloud
Twitter
Netflix
Amazon
Adobe
eBay
Hulu
Spotify
Rubikloud
4)What are active and passive “NameNodes”?
In HA (High Availability) architecture, we have two NameNodes – Active “NameNode” and Passive “NameNode”.
In HA (High Availability) architecture, we have two NameNodes – Active “NameNode” and Passive “NameNode”.
Active “NameNode” is the “NameNode” which works and runs in the cluster.
Passive “NameNode” is a standby “NameNode”, which has similar data as active “NameNode”.
When the active “NameNode” fails, the passive “NameNode” replaces the active “NameNode” in the cluster. Hence, the cluster is never without a “NameNode” and so it never fails.
Passive “NameNode” is a standby “NameNode”, which has similar data as active “NameNode”.
When the active “NameNode” fails, the passive “NameNode” replaces the active “NameNode” in the cluster. Hence, the cluster is never without a “NameNode” and so it never fails.
5)What is a checkpoint?
In brief, “Checkpointing” is a process that takes an FsImage, edit log and compacts them into a new FsImage. Thus, instead of replaying an edit log, the NameNode can load the final in-memory state directly from the FsImage. This is a far more efficient operation and reduces NameNode startup time. Checkpointing is performed by Secondary NameNode.
In brief, “Checkpointing” is a process that takes an FsImage, edit log and compacts them into a new FsImage. Thus, instead of replaying an edit log, the NameNode can load the final in-memory state directly from the FsImage. This is a far more efficient operation and reduces NameNode startup time. Checkpointing is performed by Secondary NameNode.
6)What is the port number for NameNode, Task Tracker and Job Tracker?
NameNode 50070
Job Tracker 50030
Task Tracker 50060
7)What does ‘jps’ command do?
The ‘jps’ command helps us to check if the Hadoop daemons are running or not. It shows all the Hadoop daemons i.e namenode, datanode, resourcemanager, nodemanager etc. that are running on the machine.
The ‘jps’ command helps us to check if the Hadoop daemons are running or not. It shows all the Hadoop daemons i.e namenode, datanode, resourcemanager, nodemanager etc. that are running on the machine.
8) Explain about the indexing process in HDFS?
Indexing process in HDFS depends on the block size. HDFS stores the last part of the data that further points to the address where the next part of data chunk is stored.
Indexing process in HDFS depends on the block size. HDFS stores the last part of the data that further points to the address where the next part of data chunk is stored.
9)Whenever a client submits a hadoop job, who receives it?
NameNode receives the Hadoop job which then looks for the data requested by the client and provides the block information. JobTracker takes care of resource allocation of the hadoop job to ensure timely completion.
10)What are the main configuration parameters in a “MapReduce” program?
The main configuration parameters which users need to specify in “MapReduce” framework are:
The main configuration parameters which users need to specify in “MapReduce” framework are:
Job’s input locations in the distributed file system
Job’s output location in the distributed file system
Input format of data
Output format of data
Class containing the map function
Class containing the reduce function
JAR file containing the mapper, reducer and driver classes
Job’s output location in the distributed file system
Input format of data
Output format of data
Class containing the map function
Class containing the reduce function
JAR file containing the mapper, reducer and driver classes
11)What is the purpose of “RecordReader” in Hadoop?
The “InputSplit” defines a slice of work, but does not describe how to access it. The “RecordReader” class loads the data from its source and converts it into (key, value) pairs suitable for reading by the “Mapper” task. The “RecordReader” instance is defined by the “Input Format”.
The “InputSplit” defines a slice of work, but does not describe how to access it. The “RecordReader” class loads the data from its source and converts it into (key, value) pairs suitable for reading by the “Mapper” task. The “RecordReader” instance is defined by the “Input Format”.
12)How do “reducers” communicate with each other?
This is a tricky question. The “MapReduce” programming model does not allow “reducers” to communicate with each other. “Reducers” run in isolation.
This is a tricky question. The “MapReduce” programming model does not allow “reducers” to communicate with each other. “Reducers” run in isolation.
13)What is a “Combiner”?
A “Combiner” is a mini “reducer” that performs the local “reduce” task. It receives the input from the “mapper” on a particular “node” and sends the output to the “reducer”. “Combiners” help in enhancing the efficiency of “MapReduce” by reducing the quantum of data that is required to be sent to the “reducers”.
A “Combiner” is a mini “reducer” that performs the local “reduce” task. It receives the input from the “mapper” on a particular “node” and sends the output to the “reducer”. “Combiners” help in enhancing the efficiency of “MapReduce” by reducing the quantum of data that is required to be sent to the “reducers”.
14) What are the different relational operations in “Pig Latin” you worked with?
Different relational operators are:
Different relational operators are:
for each
order by
filters
group
distinct
join
limit
order by
filters
group
distinct
join
limit
15) What is a UDF?
If some functions are unavailable in built-in operators, we can programmatically create User Defined Functions (UDF) to bring those functionalities using other languages like Java, Python, Ruby, etc. and embed it in Script file.
If some functions are unavailable in built-in operators, we can programmatically create User Defined Functions (UDF) to bring those functionalities using other languages like Java, Python, Ruby, etc. and embed it in Script file.
16)What are the components of Apache HBase?
HBase has three major components, i.e. HMaster Server, HBase RegionServer and Zookeeper.
HBase has three major components, i.e. HMaster Server, HBase RegionServer and Zookeeper.
Region Server: A table can be divided into several regions. A group of regions is served to the clients by a Region Server.
HMaster: It coordinates and manages the Region Server (similar as NameNode manages DataNode in HDFS).
ZooKeeper: Zookeeper acts like as a coordinator inside HBase distributed environment. It helps in maintaining server state inside the cluster by communicating through sessions.
HMaster: It coordinates and manages the Region Server (similar as NameNode manages DataNode in HDFS).
ZooKeeper: Zookeeper acts like as a coordinator inside HBase distributed environment. It helps in maintaining server state inside the cluster by communicating through sessions.
17) Explain about the different catalog tables in HBase?
The two important catalog tables in HBase, are ROOT and META. ROOT table tracks where the META table is and META table stores all the regions in the system.
18)Differentiate between Sqoop and distCP.
DistCP utility can be used to transfer data between clusters whereas Sqoop can be used to transfer data only between Hadoop and RDBMS.
19)How would you check whether your NameNode is working or not?
There are several ways to check the status of the NameNode. Mostly, one uses the jps command to check the status of all daemons running in the HDFS
20)What is checkpointing in Hadoop?
Checkpointing is the process of combining the Edit Logs with the FsImage (File system Image). It is performed by the Secondary NameNode………..For more Click Here
Checkpointing is the process of combining the Edit Logs with the FsImage (File system Image). It is performed by the Secondary NameNode………..For more Click Here
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