If you're looking for Hadoop Administration Interview Questions for Experienced or Freshers, you are at the right place. There are a lot of opportunities from many reputed companies in the world. According to research Hadoop Administration has a market share of about 21.5%. So, You still have the opportunity to move ahead in your career in Hadoop Administration Development. Mindmajix offers Advanced Hadoop Administration Interview Questions 2023 that helps you in cracking your interview & acquire your dream career as a Hadoop Administration Developer.
Hadoop comes with many pros. It has been observed that it offers some of the best benefits for the programmers as compared with any other framework. It makes it easy for programmers to write the code reliably and detect the same errors in the same. It is purely based on Java and thus there are no compatibility issues. As far as the matter of functions and distribution systems is concerned, Hadoop has become the number one choice of several programmers all over the world. In addition to this, handling bulk data very easily is another good thing about this framework.
Relational database management tools often fail to perform their tasks and some stages. This is common when they have to handle a large amount of data. Big data is nothing but an array of complex data sets. It is actually an approach that makes it easy for businesses to get the maximum information from their data by properly searching, analyzing, sharing, transferring, capturing, as well as visualizing the same.
Hadoop is basically an approach that makes it easy for users to handle big data without facing any problem. All the business decisions can simply be made by getting the most useful information in no time. Hadoop has been equipped with some of the best components that make it easy for the users to keep up the pace even if the data is too large. Hadoop has been equipped with two prime components and they are:
YARN stands for Yet Another Resource Negotiator
HDFS stands for Hadoop Distributed File System
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Hadoop has a powerful data storage unit which is tagged as “Hadoop Distributed File System”. Any form of data can easily be stored in it in the form of blocks. It makes use of master and slave topology. In case the need for extended storage is felt, the same can be extended to fulfill the same. Hadoop is best in this aspect.
These are actually related to storage in the Hadoop. Name Node is basically considered as a master node and is responsible for maintaining the Metadata information which is related to different blocks based on some of the factors related to them. Data Nodes are considered Slave Nodes which are mainly responsible for the storage and management of data in the basic format.
Both Resource and Node Manager are associated with the YARN. The Resource Manager is responsible for receiving the requests related to data processing. It then passes the same to the parallel Node Managers and ensures the processing has been taken place in a proper manner. On the other side, Node Manager makes sure the proper execution of task on every single Data Node.
The Secondary Name Node is responsible for this. It generally performs this task with the help of other parallel nodes and makes sure that the task has been processed at its level best. It also generated the reports related to the same which are sent along with the data for the analysis of the same in a step-wise manner.
NAS stands for Network-attached Storage and is generally regarded as the storage server which is file-level. It is connected with a server and is mainly responsible to make sure that access has been provided to a group of users. When it comes to storing and accessing the files, all the responsibilities are beard by the NAS which can be software or hardware. On the other side, HDFS is a distributed file system and is actually based on commodity hardware.
Ans: Well, it is possible to store data in a distributed manner among all the machines within a cluster. Another approach is to choose a dedicated machine for the same. The distributed approach is a good option because the failure of one machine doesn’t interrupt the entire functionality within an organization. Although backup can be created for the first case, accessing the backup data and bringing it into the main server can create a lot of time issues. Thus the second option is a good one. It is reliable. However, all the machines within a cluster need to be protected in every aspect in case of confidential data or information.
Ans: When it comes to Hadoop, it really doesn’t matter whether the data that needs to be stored is structured, unstructured, or semi-structured. Also, the schema of data is totally unfamiliar to Hadoop. On the other side, RDBMS always have structured data. It cannot process the others. The schema of the same is always known to it. When it comes to processing capabilities, RDMS has a limited number of options while Hadoop enables the same without any strict upper limit on the same. Another key difference is Hadoop is open source, while the RDBMS is licensed.
It is done mainly after the loading of the data. Sometimes it even leads to bugs but that can be managed at a later stage. Actually, it follows the scheme of the reading protocol.
Hadoop is a good option to consider for OLAP systems, data discovery, as well as for Data Analysis. Hadoop has features that make bulk data handling very easy. Because all these tasks have a lot of data to handle, the Hadoop approach can easily be trusted.
Both are good enough to be trusted. However, some features of Hadoop 2 make it an ideal choice to consider over Hadoop 1. One of the leading reasons is with 2, it is possible to run multiple applications at the same time without any issue which was not possible in the earlier versions. Also, the data handling abilities of Hadoop 2 are better and in fact quicker than the 1. In addition to this, processing takes place through a Resource Manager that always makes sure of error-free results.
They both are available in HA i.e. High Availability architecture. Active Node runs in the cluster. A secondary Node is nothing but actually a secondary Node that is considered only when the Active is not present. It is because of no other reason than this, the passive has the same data as the active. It can also be considered as a back for the data available in Active.
Yes, this can simply be done. It is one of the prime tasks of a Hadoop administrator.
A signal is periodically sent to the Name Node by the Data Node. This is actually a signal that represents all is fine with the Data Node. In case no signal is received, it is considered dead. Using the replicas created, the Name Node replaces the Data Node. However, there is not always a need to replace the whole data. Only the failed blocks can be considered.
The process of modifying FSImages is considered as a checkpoint or checkpointing for the FSImages. This is actually an operation that always makes sure of saving of time during the operations. It is performed by the Secondary Name Node.
The default replication factor is 3. Yes, it can be changed as per need.
Name Node only stores metadata that is related to the different blocks and because of this reason it needs high memory space. Data Nodes don’t need large memory space.
Well, the HDFS works more reliably with the large data when the same is stored on a single file. In Name Node, the concerned information is available in the RAM and thus it cannot deal with a large number of files. In case files are more, there will be a large amount of Metadata it needs to deal with. It is almost impossible to store such a large volume of Metadata in the RAM.
A block is nothing but a general location which is a smaller unit of a prime storage location. This is because HDFS stores data in block form. It can also be considered an independent unit.
Hadoop daemons must remain active all the time during a process is going on. Their failure causes a lot of challenges and issues. Jps Command is used to check whether they are working properly or not.
It is basically an algorithm that guides the Name Node on how the blocks are to be stored. Its main aim is to put a limit on the traffic in the network. It also manages and controls the replicas of each block.
Hadoop can easily utilize the commodity hardware which makes it easy for the users to add or to remove data nodes in case the same crashes too frequently. They can easily be scaled in case data grow at a very quick rate.
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A general rule is that it shouldn’t exceed150 bytes for the proper functioning of the Name Node.
In Hadoop 1 it is 64 MB while the same is 128 MB in the case of Hadoop 2.
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Ravindra Savaram is a Technical Lead at Mindmajix.com. His passion lies in writing articles on the most popular IT platforms including Machine learning, DevOps, Data Science, Artificial Intelligence, RPA, Deep Learning, and so on. You can stay up to date on all these technologies by following him on LinkedIn and Twitter.
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