Big Data Hadoop Developer Course with Handson

Big Data Hadoop Developer Course with Handson, available at $19.99, has an average rating of 3.45, with 89 lectures, based on 45 reviews, and has 347 subscribers.

You will learn about Basics of Big Data Detailed understanding of Big Data analytics Master HDFS, MapReduce, Hive, Pig, HBase, Yarn This course is ideal for individuals who are Programming Developers and System Administrators or Experienced working professionals or Business Intelligence, Data warehousing and Analytics Professionals or Project managers or Graduates, undergraduates eager to learn the latest Big Data technology It is particularly useful for Programming Developers and System Administrators or Experienced working professionals or Business Intelligence, Data warehousing and Analytics Professionals or Project managers or Graduates, undergraduates eager to learn the latest Big Data technology.

Enroll now: Big Data Hadoop Developer Course with Handson

Summary

Title: Big Data Hadoop Developer Course with Handson

Price: $19.99

Average Rating: 3.45

Number of Lectures: 89

Number of Published Lectures: 88

Number of Curriculum Items: 89

Number of Published Curriculum Objects: 88

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Basics of Big Data

Detailed understanding of Big Data analytics

Master HDFS, MapReduce, Hive, Pig, HBase, Yarn

Who Should Attend

Programming Developers and System Administrators

Experienced working professionals

Business Intelligence, Data warehousing and Analytics Professionals

Project managers

Graduates, undergraduates eager to learn the latest Big Data technology

Target Audiences

Programming Developers and System Administrators

Experienced working professionals

Business Intelligence, Data warehousing and Analytics Professionals

Project managers

Graduates, undergraduates eager to learn the latest Big Data technology

This course on Big Data and Hadoop is curated by Hadoop industry experts, and it covers in-depth knowledge on Big Data and Hadoop Ecosystem Tools. It is a comprehensive Big Data Hadoop?course designed by industry experts considering current industry job requirements to provide in-depth learning on big data and Hadoop Modules. This is an industry recognized Big Data Hadoop training course that is a combination of the training courses in Hadoop developer, Hadoop administrator, Hadoop testing, and analytics. This Hadoop training course will prepare you to clear big data certification.

Why should you take Big Data Hadoop?

Average Salary of Big Data Hadoop Developers is $135,000 (Indeed. com salary data)

McKinsey predicts that by 2018 there will be a shortage of 1,500,000 data experts

The Hadoop Big Data analytics market is projected to grow to USD 40.69 Billion by 2021 – MarketsandMarkets

Course Curriculum

Chapter 1: Module 1:- Introduction to Big Data and Hadoop

Lecture 1: 1.1 Introduction to BigData

Lecture 2: 1.2 Types of Data

Lecture 3: 1.3 Introduction to Hadoop

Lecture 4: 1.4 Comparison with RDBMS

Lecture 5: 1.5 Hadoop Features

Lecture 6: 1.6 Hadoop Ecosystem

Lecture 7: 1.7 Hadoop Core Components

Chapter 2: Module 2- HDFS(Hadoop Distributed File System)

Lecture 1: 2.1 Hadoop Distributed File System

Lecture 2: 2.2 HDFS Files and Blocks

Lecture 3: 2.3 HDFS Components and Architecture

Lecture 4: 2.4 HDFS File Read-Write

Lecture 5: 2.5 HDFS Commands

Chapter 3: Module 3- Mapreduce

Lecture 1: 3.1 Mapreduce

Lecture 2: 3.2 Map-Reduce Operation

Lecture 3: 3.3 Map-reduce Example

Lecture 4: 3.4 HDFS Input Splits

Lecture 5: 3.5 MapReduce Architecture

Lecture 6: 3.6 Combiners and Partitioners

Lecture 7: 3.7 MapReduce Data Flow

Lecture 8: 3.8 MapReduce Examples

Chapter 4: Module 4- Advanced Mapreduce-I

Lecture 1: 4.1 Advanced MapReduce I

Lecture 2: 4.2 GenericOptionsParser, Tool and ToolRunner

Lecture 3: 4.3 Serialization and Deserialisation

Lecture 4: 4.4 Chaining of Jobs

Lecture 5: 4.5 Distributed Cache

Lecture 6: 4.6 Counters

Lecture 7: 4.7 JUnit Testing

Lecture 8: 4.8 Schedulers

Lecture 9: 4.9 Data Compression in Hadoop

Lecture 10: 4.10 Different Input and Output Formats in MapReduce

Lecture 11: 4.11 Chain Mapping

Lecture 12: 4.12 Compression in Gzip

Lecture 13: 4.13 Distributed cache

Lecture 14: 4.14 Counters

Lecture 15: 4.15 MRUnit Test

Lecture 16: 4.16 Multiple inputs

Lecture 17: 4.17 ReadSequence File

Chapter 5: Module 5- Apache Pig

Lecture 1: 5.1 Introduction to Apache Pig

Lecture 2: 5.2 PIG Latin language

Lecture 3: 5.3 Running PIG in Different Modes

Lecture 4: 5.4 Apache PIG Architecture

Lecture 5: 5.5 Grunt Shell

Lecture 6: 5.6 Pig Latin Statements

Lecture 7: 5.7 Pig Data Model- Scalar Types

Lecture 8: 5.8 Complex Types

Lecture 9: 5.9 Arithmetic Operators

Lecture 10: 5.10 Comparison Operators

Lecture 11: 5.11 Cast Operator

Lecture 12: 5.12 Type Construction Operators

Lecture 13: 5.13 Relational Operators

Lecture 14: 5.14 Loading and Storing

Lecture 15: 5.15 Filtering Operators

Lecture 16: 5.16 Grouping and Joining Operator- Part 1

Lecture 17: 5.17 Grouping and Joining Operator- Part 2

Lecture 18: 5.18 Combining and Splitting Operators

Lecture 19: 5.19 Sorting Operators

Lecture 20: 5.20 Diagnostic Operators

Lecture 21: 5.21 Filtering Operators-Pig Streaming with Python

Chapter 6: Module 6- Apache Hive

Lecture 1: 6.1 What is Hive

Lecture 2: 6.2 Hive Use Case@ Twitter

Lecture 3: 6.3 Hive vs MapReduce

Lecture 4: 6.4 What is Hive

Lecture 5: 6.5 Advantages of HiveQL

Lecture 6: 6.6 Hive Architecture

Lecture 7: 6.7 Data Types in Hive

Lecture 8: 6.8 Hive Query Language

Lecture 9: 6.9 DDL on DataBase

Lecture 10: 6.10 DDL on Tables

Lecture 11: 6.11 Different Tables in Hive

Lecture 12: 6.12 Advanced DDL on Tables

Lecture 13: 6.13 File Format in Hive

Lecture 14: 6.14 DML- Loading Data into tables

Lecture 15: 6.15 Managing Output

Lecture 16: 6.16 HiveQL-Queries

Lecture 17: 6.17 Operators and Functions in Hive

Lecture 18: 6.18 Hive Clauses

Chapter 7: Apache HBase: NoSQL Database for Hadoop

Lecture 1: 7.1 What is HBase

Lecture 2: 7.2 HBase history

Lecture 3: 7.3 Building blocks of hbase

Lecture 4: 7.4 Column family in Hbase

Lecture 5: 7.5 Storage of Column Family

Lecture 6: 7.6 Data Model in HBase

Lecture 7: 7.7 Timestamp as Versions

Lecture 8: 7.8 Getting Started with HBase Shell

Lecture 9: 7.9 DDL in HBase part-1

Lecture 10: 7.10 DDL in HBase part-2

Lecture 11: 7.11 DDL in HBase part-3

Lecture 12: 7.12 DML in HBase

Instructors

Edionik Solutions
Serving Technology Simply

Rating Distribution

1 stars: 3 votes

2 stars: 5 votes

3 stars: 10 votes

4 stars: 13 votes

5 stars: 14 votes

Frequently Asked Questions

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