Become a Big Data Hadoop Developer from scratch

Become a Big Data Hadoop Developer from scratch, available at $19.99, has an average rating of 3.4, with 82 lectures, based on 33 reviews, and has 410 subscribers.

You will learn about learn introduction to hadoop Hadoop Distributed File System(HDFS) MapReduce(MR) Run MapReduce Application using JAVA Run Word Count example in JAVA Run Max.Temp. Hadoop MapReduce program in JAVA HDFS Commands for accessing Hadoop File System Running Queries in HBase Different operations in HBase using JAVA API HBase Architecture Apache HIVE Apache PIG This course is ideal for individuals who are Professionals who want to learn Hadoop or Data Analyst or Hadoop Beginners or Professionals who want to make MapReduce Application It is particularly useful for Professionals who want to learn Hadoop or Data Analyst or Hadoop Beginners or Professionals who want to make MapReduce Application.

Enroll now: Become a Big Data Hadoop Developer from scratch

Summary

Title: Become a Big Data Hadoop Developer from scratch

Price: $19.99

Average Rating: 3.4

Number of Lectures: 82

Number of Published Lectures: 82

Number of Curriculum Items: 82

Number of Published Curriculum Objects: 82

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

learn introduction to hadoop
Hadoop Distributed File System(HDFS)
MapReduce(MR)
Run MapReduce Application using JAVA
Run Word Count example in JAVA
Run Max.Temp. Hadoop MapReduce program in JAVA
HDFS Commands for accessing Hadoop File System
Running Queries in HBase
Different operations in HBase using JAVA API
HBase Architecture
Apache HIVE
Apache PIG

Who Should Attend

Professionals who want to learn Hadoop
Data Analyst
Hadoop Beginners
Professionals who want to make MapReduce Application

Target Audiences

Professionals who want to learn Hadoop
Data Analyst
Hadoop Beginners
Professionals who want to make MapReduce Application

Apache Hadoop is an open-source software framework for distributed storage and distributed processing of large data on computer clusters built from commodity hardware.

In this course we’ll discuss about several important aspects of Hadoop like HDFS(Hadoop Distributed File System), MapReduce, Hive, HBase and Pig.

First we’ll talk about Overview of Big data means what is Big Data, Facts of Big Data, Scenarios, Hadoop cluster architecture. Then we’ll move towards HDFS, Components of HDFS and its architecture, NameNode, Secondary NameNode and DataNode.

Next module is about MapReduce. In this we’ll talk about Map Phase and Reduce Phase, Architecture of MapReduce, Combiners and Reducers.�

Next module is about PIG. In this we’ll see what is Apache Pig, its importance, Pig Latin language, and where to avoid Pig.

Them we’ll talk about HBase, we’ll talk about its use cases, general commands in HBase, DDL in HBase, DML in HBase, How to create, delete and integrate table in HBase and lot more.

So start learning Hadoop today.

Course Curriculum

Chapter 1: Module1

Lecture 1: 1.1 Overview of BigData

Lecture 2: 1.2 Facts about Big Data

Lecture 3: 1.3 Big Data Scenarios

Lecture 4: 1.4 Introduction to Hadoop

Lecture 5: 1.7 Difference between RDBMS and Hadoop

Lecture 6: 1.8 Cluster Modes in Hadoop

Lecture 7: 1.9 Hadoop Ecosystem

Lecture 8: 1.10 HDFS Daemons and Mapreduce daemons

Lecture 9: 1.11 HADOOP CLUSTER ARCHITECTURE

Chapter 2: Module 2

Lecture 1: 2.1 HDFS

Lecture 2: 2.2 HDFS FILES AND BLOCKS

Lecture 3: 2.3 HDFS Components and Architecture

Lecture 4: 2.4 NameNode Secondary NameNode and DataNode

Lecture 5: 2.5 HDFS FILE READ AND WRITE OPERATIONS

Lecture 6: HDFS Commands

Chapter 3: Module 3

Lecture 1: 3.1 Basics of MapReduce

Lecture 2: 3.2 Map Phase and Reduce Phase

Lecture 3: 3.3 JOB Submission Flow in MapReduce

Lecture 4: 3.4 HDFS BLOCK AND INPUTSPLIT

Lecture 5: 3.5 MAPREDUCE ARCHITECTURE

Lecture 6: 3.6 COMBINER and REDUCERS

Lecture 7: 3.7 MapReduce DataFlow

Lecture 8: Wordcount Prgram in MapReduce

Lecture 9: Maximum Temprature Program part-1

Lecture 10: Maximum Temprature Program part-2

Chapter 4: Module 4

Lecture 1: 4.1 Map, Reduce and Driver

Lecture 2: 4.2 Difference between New API and Old API

Lecture 3: 4.3 GenericOptionsParser, Tool and ToolRunner

Lecture 4: 4.4 Writables in Hadoop

Lecture 5: 4.5 Serialization and Deserialization in Hadoop

Lecture 6: 4.6 Schedulers and Distributed Cache

Lecture 7: 4.7 Sequence File

Lecture 8: 4.9 MRUNIT TESTING

Chapter 5: Module 5

Lecture 1: 5.1 Apache Pig

Lecture 2: 5.2 Importance of Apache PIG

Lecture 3: 5.3 Apache Pig vs MapReduce

Lecture 4: 5.4 Where Pig is Best Suited

Lecture 5: 5.5 Where to avoid Pig

Lecture 6: 5.6 PIG Latin Language

Lecture 7: 5.7 Running PIG in Different Modes

Chapter 6: Module 6

Lecture 1: 6.1 What is HBase

Lecture 2: 6.2 UseCases of Apache PIG

Lecture 3: 6.3 What is NoSQL Databases

Lecture 4: 6.4 Characteristics of NoSQL Databases

Lecture 5: 6.5 Categories of NoSQL Databases

Lecture 6: 6.6 Difference between NoSQL and RDBMS

Lecture 7: 6.7 Characteristics of Apache HBase

Lecture 8: 6.8 Comparison between HDFS and HBase

Lecture 9: 6.9 Where to use HBase

Lecture 10: 6.10 Where to avoid HBase

Lecture 11: 6.11 Building Blocks of Apache HBase

Lecture 12: 6.12 Column Family in HBase

Lecture 13: 6.13 Storage of Column Family

Lecture 14: 6.14 TimeStamp as Version

Lecture 15: 6.15 General Commands in HBase

Lecture 16: 6.16 DDL in HBase

Lecture 17: 6.17 DDL in HBase-2

Lecture 18: 6.18 DML in HBase

Lecture 19: 6.19 Core Components of HBase

Lecture 20: 6.20 Need of Zookeeper

Lecture 21: 6.21 How Client interacts with HBase Cluster

Lecture 22: 6.22 Bloom Filter in HBase

Lecture 23: 6.23 HFile in HBase

Lecture 24: 6.24 Write Ahead Log in HBase

Lecture 25: 6.25 Creating Table in Apache HBase Using JAVA API

Lecture 26: 6.26 Deleating Table in Apache HBase Using JAVA API

Lecture 27: 6.27 Disabling Table in Apache HBase Using JAVA API

Lecture 28: 6.28 Inserting data in Table in Apache HBase Using JAVA API

Lecture 29: 6.29 How to list and Enable a table in Apache HBase Using JAVA API

Lecture 30: 6.30 Integrating Apache HBase with Pig Tool part-I

Lecture 31: 6.31 Integrating HBase with Apache Pig -II

Lecture 32: 6.32 Integrating HBase with Apache PIG-III

Lecture 33: 6.33 Integrating HBase with Apache HIve

Lecture 34: 6.34 Integration of Hive Tool with Apache HBase

Lecture 35: 6.35 Integrating HBase with Apache Sqoop

Lecture 36: 6.36 Integration of Apache HBase with Sqoop Tool part-2

Lecture 37: 6.37 Filtering Operations in HBase

Lecture 38: 6.38 KeyOnlyFilter in HBase

Lecture 39: 6.39 FirstKeyOnlyFilter and PrefixFilter in HBase

Lecture 40: 6.40 ColumnCountGetFilter and PageFilter

Lecture 41: 6.42 ColumnPrefixFilter and MultipleColumnPrefixFilters

Lecture 42: 6.43 ValueFilter

Instructors

Elementary Learners
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Rating Distribution

1 stars: 2 votes
2 stars: 8 votes
3 stars: 7 votes
4 stars: 8 votes
5 stars: 8 votes

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