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Hadoop Practice Exam

Hadoop


The demand for Hadoop professionals is increasing due to companies inclining towards Big Data. The basic job of a Hadoop professional is to analyse Big Data and extract meaningful information out of it. Hadoop has a special ability that allows its users to store all sets of data in a distributed way. This method allows you to store a large amount of data efficiently and hence make the analysing process more flexible.


Roles and Responsibilities

Some of the major roles and responsibilities of Hadoop professionals include the following:

Responsible for the documentation, design, development, and architecture of Hadoop applications

Handling the installation, configuration, and supporting of Hadoop

Write MapReduce coding for Hadoop clusters

Design web applications for querying data 

Converting hard and complex techniques into detailed designs

Performing testing of software prototypes and transfer to the operational team

Maintaining data security and privacy

Perform analysis of a large amount of data stores and derive insights


Who should take this exam?

Software Professionals

Analytics Professionals

ETL developers

Project Managers

Architects

Testing Professionals


Skills Required

Analytical skills

Communication skills

Critical thinking

Detail-oriented

SQL

NoSQL


Career Prospects

Hadoop architect

Hadoop administrator

Hadoop tester


Table of Content

The Hadoop exam covers the following topics - 

Big Data

Learning Big Data Definition

Learning Big Data Types

Big Data Source

Learning Big Data Challenges

Big Data Benefits

Learning Big Data Applications

Netflix Application

Learning Big Data Case Studies

Apache Hadoop

Introduction to Hadoop

Learning Advantages & Disadvantages

Learning the history of Hadoop Project

Need for Hadoop

Hadoop Architecture

Learning RDBMS vs Hadoop

Vendor Comparison

Learning Hardware Recommendations

Hadoop Installation

HDFS

Learning HDFS Basics

HDFS Architecture

Data Read and Write Process

Learning HDFS Permissions

Data Replication

Learning HDFS Accessibility

HDFS Filesystem Operations

Learning HDFS Interfaces

Heartbeats

Learning Rack Awareness

distcp

MapReduce

Learning MapReduce Basics

MapReduce Work Flow

Learning MapReduce Framework

Learning Hadoop Data Types

MapReduce Internals

Learning Job Formats

Learning Debugging and Profiling

Learning Distributed Cache

Combiner Functions

Learning MapReduce Streaming

MapReduce Counters, Sorting and Joins

YARN

Learning YARN Infrastructure

YARN ResourceManager

Learning YARN ApplicationMaster

YARN NodeManager

Learning YARN Container

Pig

Learning Pig Architecture

Pig Installation and Modes

Learning Grunt and Pig Script

Pig Latin Commands

Learning UDF and Data Processing Operator

HBase

Learning HBase Architecture

HBase Installation

Learning HBase Configuration

HBase Schema Design

Learning HBase Commands

MapReduce Integration

Learning HBase Security

Sqoop and Flume

Learning Sqoop

Learning Flume

Hive

Learning Hive Architecture

Hive shell

Learning Hive Data types

Learning HiveQL

Hadoop Workflow

Learning Apache Oozie

Hadoop Cluster Management

Learning Cluster Planning

Cluster Installation and Configuration

Learning Cluster Testing

Cluster Benchmarking

Learning Cluster Monitoring

Hadoop Administration

Learning dfsadmin, fsck and balancer

Hadoop Logging

Learning Hadoop Data Backup

Add and removal of nodes

Hadoop Security

Learning Authentication

Learning Data Confidentiality

Learning Configuration

NextGen Hadoop

Learning HDFS HA

Learning HDFS Federation


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