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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