Hadoop is an open-source software framework that stores massive amounts of data. It runs applications on clusters of commodity hardware. Hadoop can hand virtually unlimited concurrent jobs or tasks. Hadoop also provides a vast amount of storage space for any data.
Big data & Hadoop have excellent processing power. Especially in the energy industry, analytics powered by Hadoop is used for predictive maintenance. It feeds data into big data programs, with input from the Internet of Things(IoT) . The demand for Hadoop experts is steadily on the rise. The Common question arises on how to prepare for a Hadoop interview or Hadoop admin interview. So, it is a brilliant idea to get upskilled as a Hadoop expert. Hadoop experts get a lucrative salary in the Big Data job market.
The interview panel asks the candidates who apply for hadoop developer or hadoop testing a few general questions as well as hadoop scenario based questions to check knowledge. So, a comprehensive list of probable questions and their answers helps prospective applicants be prepared for such interviews with a clearcutdifference between big data and data science. We will also look into questions from the entire Hadoop ecosystem, which includes HDFS, MapReduce, YARN, Hive, Pig, HBase, and Sqoop.
A collection of massive and complicated is known as big data. It poses difficulty in processing traditional data. Big Data has provided a suitable opportunity for companies. It provides companies with a suitable opportunity for advanced business-making capacity.
Big Data helps companies to derive value successfully from their data. The Five V's of data is as follows:
Apache Hadoop is a framework that provides different tools and services to process Big Data. The tools and services also help people to store Big Data.
ApachenHadoop has two main components:
Hadoop helps people analyze Big data. Experts can make some business decisions with Big data through Hadoop. Traditional systems do not allow people to take such effective decisions with Big Data.
The storage unit of Hadoop is known as HDFS. It stands for Hadoop Distributed File System.It follows the topology of the master and slave. It stores different types of data in a distributed environment as blocks.
The components of HDFS are as follows:-
The processing framework of Hadoop is known as Yarn. It stands for Yet Another Resource Negotiator. It manages the resources.
Two components of Yarn are as follows:-
NameNode and Data Node are the two components of HDFS.
NameNodenmaintains the metadata information for the blocks of data stored in HDFS. Itnmay be described as the master node in the distributed environment. It managesnall the DataNodes.
Datanodesnare responsible for storing data in HDFS. They are the slave NODES.
ResourceManager and NodeManager are the two components of Yarn. The node manager is installed in all the data nodes. It is responsible for executing all the tasks on every Datanode.
Resourcenmanagers receive processing requests. They then transfer the parts of thenrequests to the corresponding NodeManagers, where the actual processing occurs.
Hadoop is written in Java.
Many programming developers use Python because of its supporting libraries for data analytics tasks. Python is a flexible programming language which has many libraries and resources. Many software companies need their employees to be well-versed in Python. This programming language helps in producing and reading codes. Many companies use Python with Hadoop in the following ways:
Amazon recommends different products to its users, based on their past buying history.nPython has been used to build the machine learning engine of Amazon. Itninteracts with Hadoop Ecosystem to deliver the best quality productnrecommendation system.
Facebooknhas gained unprecedented popularity in the field of social media. It enablesnHDFS to extract extensive image-based unstructured data. Then, it uses Python as the backend language for image processing applications. Facebook also usesnHadoop streaming API to edit and access the data.
Hadoop can run in three modes, which are as follows:
A name node server holds two types of metadata. They are as follows:
Below are the top trending related interview questions
"NameNode" is the single point of failure in Hadoop 1.x. Active and Passive NameNodes are present in Hadoop 2.x
Hadoop utilizes commodity hardware. It leads to regular "Datanode" crashing in a Hadoop cluster.
Sincenthe volume of data is ever-increasing, Framework is the ease of scale,naccording to it. So, a Hadoop administrator has the critical duty to add andnremove data nodes from a Hadoop cluster.
Sometimes, a node appears to execute a task slowly. In that case, a master node may execute another instance of the same task to another node redundantly. In that case, the task that finishes first is accepted. The other one is killed. This process is "speculative execution" in Hadoop.
A "RecordReader" loads the data from its source. It then converts the data into suitable pairs so that the "Mapper" task may read it.
It is easy to write a custom partitioner for a Hadoop job, by following the steps mentioned below:
A few companies like Yahoo, Amazon, Netflix, and Twitter use Hadoop.
Hadoop framework works on two components as follows-
The distribution of Hadoop has a generic application programming interface. This interface writes Map and Reduce jobs in programming languages like Python and Ruby. It is known as Hadoop streaming.
The most common input formats of Hadoop are as follows:-
Several factors choose which file people use to store and process data in Apache Hadoop. The factors are as follows:
The interface between the Hadoop cluster and the external network is known as an edge node. It is used to run client network and client administration tools.
Side data refers to the extra read-only data that a Hadoop job needs to process the original data set. Hadoop has two side data distribution techniques:
Dual processors with 4GB Ram or 8GB RAM, which uses EC memory, form the best hardware configuration. ECC memory is the best for running Hadoop. Users who have used non-ECC memories have experienced different checksum errors. The hardware configuration is also essential in managing workflow requirements. It is liable to sudden change.
The advantages of using Hadoop are as follows:
The different features of Hadoop are as follows:
There are some fundamental differences between Hoop and RDBMS. They are as follows:
RDBMS | Hadoop | |
Data Types |
RDBMS relies on the structured data and the schema of the data is always known. | Any kind of data can be stored into Hadoop i.e. Be it structured, unstructured or semi-structured. |
Processing | RDBMS provides limited or no processing capabilities. | Hadoop allows us to process the data which is distributed across the cluster in a parallel fashion. |
Schema on Read Vs. Write | RDBMS is based on ‘schema on write’ where schema validation is done before loading the data. | On the contrary, Hadoop follows the schema on read policy. |
Read/Write Speed | In RDBMS, reads are fast because the schema of the data is already known. | The writes are fast in HDFS because no schema validation happens during HDFS write. |
Cost | Licensed software, therefore, I have to pay for the software. | Hadoop is an open source framework. So, I don’t need to pay for the software. |
Best Fit Use Case | RDBMS is used for OLTP (Online Trasanctional Processing) system. | Hadoop is used for Data discovery, data analytics or OLAP system. |
Hadoop has a few disadvantages. They are as follows:
There are a few fundamental differences between Hadoop 2 and Hadoop 3. They are as follows:
Hadoop 2 | Hadoop 3 |
Hadoop 2 has features that help it overcome SPOF (Single Point of Failure). | Hadoop 3 doesn't require manual intervention to overcome SPOF |
Hadoop 2 supports the minimum Java version of Java 7 | Hadoop 3 supports the minimum Java version of Java 8. |
HDFS has 200% in overhead storage space in the case of Hadoop 2 | Hadoop 3 has 50% in overhead storage space. |
Hadoop 2 handles fault tolerance through replication | Hadoop 3 handles fault tolerance by Erasure coding. |
Hadoop 2 uses an HDFS balancer for data balancing. | Hadoop 3 uses an Intra-date node balancer for a data balancer. |
Hadoop uses Kerberos to achieve security. The client needs to take three steps to access the service while using Kerberos. In each of the steps, a message is exchanged with the server. The steps are as follows:
In Hadoop, throughput is the amount of work done within a specified time.
In Hadoop, the .jbs command helps people check whether the Hadoop is running or not.
Hdfs stand for Hadoop Distributed file system. It is the min storage system of Hadoop.
Itnstores large files running on a cluster of commodity hardware. HDFS follows thenprinciple of big files in less number, instead of many small files.
Multiple writers cannot write in an HDFS file simultaneously. The model which Apache Hadoop follows is known as a single writer multiple reader model. NameNode grants a lease to the client who opens a file to write. If another client wants to write in that file, it seeks permission from NameNode for writing operation. Then, NameNode checks whether the access to write has been granted to someone else earlier. If the lease has already been granted to someone else earlier, NameNode will reject the second client's writing request.
A continuous location on the hard drive, known as block stores data. FileSystem generally stores the data as a collection of blocks. HDFS stores each file as a block, and distributes it across the Hadoop cluster. The default size of the data block is 128mb in HDFS. We may configure it as per our requirement. The default size of each data block is 1258mb, by default, to reduce the disk's size. The block size cannot be so large that the system waits for a long time for the last unit of data to finish processing.
Hadoop has an impressive way of indexing. Initially, the Hadoop framework stores the data according to the block size. Then, HDFS continues to store the last part of the data. It says where the next part of the data will be.
Conclusion: Hadoop is on its way to being the future of technology. The above-discussed Hadoop Interview questions and answers will help a candidate face the interview panel confidently. However, these questions just provide a basic overview of the interview. The candidates need to have a clear concept and an in-depth knowledge of Hadoop.
We had to spend lots of hours researching and deliberating on what are the best possible answers to these interview questions. We would love to invite people from the IT industry freshers, experienced to understand interview FAQ’s to excel the performance.
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