Hadoop Interview questions / Big Data Interview Questions For Freshers and Experienced : The Hadoop was developed by Apache Software Foundation which was written in the Java programming language. It was initially released on 10th December 2011 but after some modifications it was stable released on 25th August 2016. Any kind of data can be stored into Hadoop i.e. structured, unstructured or semi-structured. Apache Hadoop allows us to process the data which was distributed across the cluster in a parallel fashion. It follows the schema on read policy. Hadoop is an open source framework. So, one can’t need to pay for the software. Hadoop is used for Data discovery, data analytics or OLAP system. Here you can check list of Hadoop interview questions and answers which are frequently asked by the mnc companies.
Hadoop Interview Questions and Answers For Freshers and Experienced
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Hadoop interview questions to get an edge in the escalating Big Data market where global and local enterprises, big or small, all are looking for quality Big Data and Hadoop experts.
As a big data professional, it is necessary to know the right buzzwords, learn the right technologies and prepare the right answers to commonly asked Hadoop interview questions.
Big data is defined as the huge amount of structured, unstructured or semi-structured data that has massive potential for mining; it is characterized by its high velocity, volume and variety that require cost effective and innovative methods for information processing to draw meaningful business insights.
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.
Hadoop framework is utilization of commodity hardware. The prominent feature of Hadoop Framework is the ease of scale in accordance with the rapid growth in data volume. The most common task of a Hadoop administrator is to commission (Add) and decommission (Remove) “Data Nodes” in a Hadoop Cluster.
The Core component modules of Apache Hadoop are Hive, HDFS, YARN, MapReduce, Hadoop Common , Pig, Flume, Sqoop, , etc.
Apache Hive It is a data warehouse infrastructure which was built on the top of Hadoop, for providing data summarization, query and analysis, and it also gives an SQL link interface to query data stored in various databases and file systems which was integrated with the Hadoop.
HDFS (Hadoop Distributed File System) is the storage unit of Hadoop. It is responsible for storing different kinds of data as blocks on commodity machines providing very high aggregate bandwidth across the cluster.
YARN (Yet Another Resource Negotiator) is the processing framework in Hadoop, which manages resources and provides an execution environment to the processes.
MapReduce This is a java based programming model for large scale data processing. MapReduce distributes the workload into various tasks that can run in parallel.
Hadoop Common contains libraries and utilities needed by the other Hadoop modules.
Apache Pig is a platform, used to analyze large data sets, data manipulation operations which can be performed very easily in Hadoop using Apache Pig. It provides various built-in operators like join, sort, filter, etc. to read, write, and process large data sets.
Top 100 Hadoop Interview Questions For Freshers and Experienced:
1. How will you add/delete a Node to the existing cluster?
A) Add: Add the host name/Ip address in dfs.hosts/slaves file and refresh the cluster with $hadoop dfsamin refreshNodes
Delete: Add the hostname/Ip address to dfs.hosts.exclude/remove the entry from slaves file and refresh the cluster with $hadoop dfsamin -refreshNodes
2. What is SSH? What is the use of it In Hadoop?
A) Secure Shell.
3. How will you setup Password-less SSH?
A) search in this site
4. How will you format the HDFS? How frequently it will be done?
A) $hadoop namnode -format.
Note: Format had to be done only once that to during initial cluster setup
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