Apache Hadoop and Mapreduce Interview Questions and Answers

Apache Hadoop and Mapreduce Interview Questions and Answers, available at $19.99, has an average rating of 3.2, with 130 lectures, based on 5 reviews, and has 92 subscribers.

You will learn about 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. This course is ideal for individuals who are 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 It is particularly useful 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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Summary

Title: Apache Hadoop and Mapreduce Interview Questions and Answers

Price: $19.99

Average Rating: 3.2

Number of Lectures: 130

Number of Published Lectures: 130

Number of Curriculum Items: 130

Number of Published Curriculum Objects: 130

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will 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.

Who Should Attend

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

Target Audiences

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

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

Course Curriculum

Chapter 1: Section 1

Lecture 1: Introduction

Lecture 2: How to unzip .gz files in a new directory in hadoop?

Lecture 3: Scenario Based Question

Lecture 4: How does Hadoop Namenode failover process works?

Lecture 5: Scenario Based Question

Lecture 6: Tips to Improve Your Course Taking Experience

Lecture 7: How can we initiate a manual failover when automatic failover is configured?

Lecture 8: When not use Hadoop?

Lecture 9: Is there a simple command for hadoop that can change the name of a file ?

Lecture 10: When To Use Hadoop?

Lecture 11: Scenario Based Question

Chapter 2: Section 2

Lecture 1: Can I have multiple files in HDFS use different block sizes?

Lecture 2: Scenario Based Question

Lecture 3: As we talk about Hadoop is Highly scalable how well does it Scale?

Lecture 4: What platforms and Java versions does Hadoop run on?

Lecture 5: What kind of hardware scales best for Hadoop?

Lecture 6: Is there an easy way to see the status and health of a cluster?

Lecture 7: Scenario Based Question

Lecture 8: Scenario Based Question

Lecture 9: Scenario Based Question

Lecture 10: Scenario Based Question

Chapter 3: Section 3

Lecture 1: I am seeing connection refused in the logs. How do I troubleshoot this?

Lecture 2: Does Hadoop require SSH?

Lecture 3: What does NFS: Cannot create lock on (some dir) mean?

Lecture 4: Scenario Based Question

Lecture 5: Scenario Based Question

Lecture 6: Scenario Based Question

Lecture 7: Scenario Based Question

Lecture 8: Scenario Based Question

Lecture 9: Scenario Based Question

Lecture 10: Scenario Based Question

Chapter 4: Section 4

Lecture 1: What is the purpose of the secondary name-node?

Lecture 2: Scenario Based Question

Lecture 3: How do I set up a hadoop node to use multiple volumes?

Lecture 4: Scenario Based Question

Lecture 5: Does HDFS make block boundaries between records?

Lecture 6: Does Wildcard characters work correctly in FsShell?

Lecture 7: What does file could only be replicated to 0 nodes, instead of 1 mean?

Lecture 8: Scenario Based Question

Lecture 9: What happens when two clients try to write into the same HDFS file?

Lecture 10: How to limit Data nodes disk usage?

Chapter 5: Section 5

Lecture 1: Scenario Based Question

Lecture 2: Scenario Based Question

Lecture 3: On an individual data node, how do you balance the blocks on the disk?

Lecture 4: Scenario Based Question

Lecture 5: Difference between hadoop fs -put and hadoop fs -copyFromLocal?

Lecture 6: Scenario Based Question

Lecture 7: How to check HDFS Directory size?

Lecture 8: Scenario Based Question

Lecture 9: On what concept the Hadoop framework works?

Lecture 10: What is Hadoop streaming?

Chapter 6: Section 6

Lecture 1: Explain about the process of inter cluster data copying.?

Lecture 2: Scenario Based Question

Lecture 3: Differentiate between Structured and Unstructured data?

Lecture 4: Explain the difference between NameNode, Backup Node and Checkpoint NameNode?

Lecture 5: How can you overwrite the replication factors in HDFS?

Lecture 6: What is the process to change the files at arbitrary locations in HDFS?

Lecture 7: Explain about the indexing process in HDFS?

Lecture 8: What is a rack awareness and on what basis is data stored in a rack?

Lecture 9: What happens to a NameNode that has no data?

Lecture 10: Scenario Based Question

Chapter 7: Section 7

Lecture 1: Scenario Based Question

Lecture 2: Whenever a client submits a hadoop job, who receives it?

Lecture 3: What do you understand by edge nodes in Hadoop?

Lecture 4: What are real-time industry applications of Hadoop?

Lecture 5: What all modes Hadoop can be run in?

Lecture 6: Explain the major difference between HDFS block and InputSplit?

Lecture 7: What are the most common Input Formats in Hadoop?

Lecture 8: What is Speculative Execution in Hadoop?

Lecture 9: What is Fault Tolerance?

Lecture 10: What is a heartbeat in HDFS?

Chapter 8: Section 8

Lecture 1: How to keep HDFS cluster balanced?

Lecture 2: How to deal with small files in Hadoop?

Lecture 3: Scenario Based Question

Lecture 4: What type of problems can mapreduce solve?

Lecture 5: What is the difference between Hadoop Map Reduce and Google Map Reduce?

Lecture 6: How to get the input file name in the mapper in a Hadoop program?

Lecture 7: Scenario Based Question

Lecture 8: Scenario Based Question

Lecture 9: Scenario Based Question

Lecture 10: Can you set number of map task in Map reduce?

Chapter 9: Section 9

Lecture 1: If your Mapreduce Job launches 20 task for 1 job can you limit to 10 task?

Lecture 2: Scenario Based Question

Lecture 3: What is Shuffling and Sorting in Hadoop MapReduce?

Lecture 4: How do I submit extra content (jars, static files, etc) for Mapreduce job to use

Lecture 5: How do I get my MapReduce Java Program to read the Clusters set configuration?

Lecture 6: Explain what happens when Hadoop spawned 50 tasks for a job and one of the task

Lecture 7: What is OutputCommitter?

Lecture 8: What is RecordReader in a Map Reduce?

Lecture 9: What is a MapReduce Combiner?

Lecture 10: What do you understand by the term Straggler ?

Instructors

Bigdata Engineer
Bigdata Engineer

Rating Distribution

1 stars: 1 votes

2 stars: 0 votes

3 stars: 2 votes

4 stars: 1 votes

5 stars: 1 votes

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