Apache Hadoop and Mapreduce Interview Questions and Answers | Udemy


Apache Hadoop and Mapreduce Interview Questions and Answers | Udemy
English | Size: 576.73 MB
Genre: eLearning

What you’ll learn
By attending this course you will get to know frequently and most likely asked Programming, Scenario based, Fundamentals, and Performance Tuning based Question asked in Apache Hadoop and Mapreduce Interview along with the answer
This will help Bigdata Career Aspirants to prepare for the interview.
During your Scheduled Interview you do not have to spend time searching the Internet for Apache Hadoop and Mapreduce Interview questions.
We have already compiled the most frequently asked and latest Apache Hadoop and Mapreduce Interview questions in this course.

Apache Hadoop and Mapreduce Interview Questions has 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

YARN REST APIs

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

Who this course is for:
This course is designed for Apache Hadoop and Mapreduce Job seeker with 6 months to 2 years of Experience in Apache Hadoop and Mapreduce or Big data Hadoop Development and looking out for new job as Developer,Bigdata Engineers or Developers, Software Developer, Software Architect, Development Manager

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