Apache Hadoop and Mapreduce Interview Questions and Answers

Apache Hadoop and Mapreduce  Interview Questions and Answers (120+ FAQ)

Language: English

Instructors: Bigdata Engineer

$15 66.67% OFF



Why this course?


Apache Hadoop and Mapreduce Interview Questions have a collection of 120+ questions with answers asked in the interview for freshers and experienced (Programming, Scenario-Based, Fundamentals, Performance Tuning based Question and Answer).

This course is intended to help Apache Hadoop and Mapreduce Career Aspirants to prepare for the interview.

We are planning to add more questions in upcoming versions of this course.

The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures.

Hadoop MapReduce is a software framework for easily writing applications which process vast amounts of data (multi-terabyte data-sets) in-parallel on large clusters (thousands of nodes) of commodity hardware in a reliable, fault-tolerant manner.

A MapReduce job usually splits the input data-set into independent chunks which are processed by the map tasks in a completely parallel manner. The framework sorts the outputs of the maps, which are then input to the reduce tasks. Typically both the input and the output of the job are stored in a file-system. The framework takes care of scheduling tasks, monitoring them and re-executes the failed tasks.

Typically the compute nodes and the storage nodes are the same, that is, the MapReduce framework and the Hadoop Distributed File System (see HDFS Architecture Guide) are running on the same set of nodes. This configuration allows the framework to effectively schedule tasks on the nodes where data is already present, resulting in very high aggregate bandwidth across the cluster.

Course Consist of the Interview Question on the following Topics

  • Single Node Setup
  • Cluster Setup
  • Commands Reference
  • FileSystem Shell
  • Compatibility Specification
  • Interface Classification
  • FileSystem Specification
  • Common
  • CLI Mini Cluster
  • Native Libraries
  • HDFS
  • Architecture
  • Commands Reference
  • NameNode HA With QJM
  • NameNode HA With NFS
  • Federation
  • ViewFs
  • Snapshots
  • Edits Viewer
  • Image Viewer
  • Permissions and HDFS
  • Quotas and HDFS
  • Disk Balancer
  • Upgrade Domain
  • DataNode Admin
  • Router Federation
  • Provided Storage
  • MapReduce
  • Distributed Cache Deploy
  • Support for YARN Shared Cache
  • MapReduce REST APIs
  • MR Application Master
  • MR History Server
  • YARN
  • Architecture
  • Commands Reference
  • ResourceManager Restart
  • ResourceManager HA
  • Node Labels
  • Node Attributes
  • Web Application Proxy
  • Timeline Server
  • Timeline Service V.2
  • Writing YARN Applications
  • YARN Application Security
  • NodeManager
  • Using CGroups
  • YARN Federation
  • Shared Cache
  • YARN UI2
  • Introduction
  • Resource Manager
  • Node Manager
  • Timeline Server
  • Timeline Service V.2
  • YARN Service
  • Yarn Service API
  • Hadoop Streaming
  • Hadoop Archives
  • Hadoop Archive Logs
  • DistCp
  • Hadoop Benchmarking
  • Reference
  • Changelog and Release Notes
  • Configuration
  • core-default.xml
  • hdfs-default.xml
  • hdfs-rbf-default.xml
  • mapred-default.xml
  • yarn-default.xml
  • Deprecated Properties

Course Curriculum

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