Big Data Hadoop Developer Course with Handson
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Big Data Hadoop Developer Course with Handson

Learn HDFS, HIVE, HBASE, PIG, YARN, Advanced MapReduce concept 1, 2 and other important technologies
New
0.0 (0 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
25 students enrolled
Created by Edionik Solutions
Last updated 9/2017
English
Current price: $12 Original price: $100 Discount: 88% off
3 days left at this price!
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Includes:
  • 11 hours on-demand video
  • 5 Supplemental Resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion

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What Will I Learn?
  • Basics of Big Data
  • Detailed understanding of Big Data analytics
  • Master HDFS, MapReduce, Hive, Pig, HBase, Yarn
View Curriculum
Requirements
  • Basics of UNIX
  • LINUX
  • Knowledge of Core Java
  • Basics of SQL
Description

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

Who is the target audience?
  • 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
Compare to Other Big Data Courses
Curriculum For This Course
88 Lectures
10:47:55
+
Module 1:- Introduction to Big Data and Hadoop
7 Lectures 47:53


1.3 Introduction to Hadoop
05:51

1.4 Comparison with RDBMS
03:47

1.5 Hadoop Features
02:58

1.6 Hadoop Ecosystem
12:30

1.7 Hadoop Core Components
06:38
+
Module 2- HDFS(Hadoop Distributed File System)
5 Lectures 39:26
2.1 Hadoop Distributed File System
05:59

2.2 HDFS Files and Blocks
05:46

2.3 HDFS Components and Architecture
08:56

2.4 HDFS File Read-Write
06:14

2.5 HDFS Commands
12:31
+
Module 3- Mapreduce
8 Lectures 50:31
3.1 Mapreduce
07:54

3.2 Map-Reduce Operation
05:09

3.3 Map-reduce Example
03:56

3.4 HDFS Input Splits
03:34

3.5 MapReduce Architecture
04:33

3.6 Combiners and Partitioners
06:45

3.7 MapReduce Data Flow
02:56

3.8 MapReduce Examples
15:44
+
Module 4- Advanced Mapreduce-I
17 Lectures 01:49:04
4.1 Advanced MapReduce I
03:11

4.2 GenericOptionsParser, Tool and ToolRunner
02:56

4.3 Serialization and Deserialisation
03:25

4.4 Chaining of Jobs
02:44

4.5 Distributed Cache
01:35

4.6 Counters
04:53

4.7 JUnit Testing
01:37

4.8 Schedulers
03:01

4.9 Data Compression in Hadoop
07:21

4.10 Different Input and Output Formats in MapReduce
04:27

4.11 Chain Mapping
14:25

4.12 Compression in Gzip
06:57

4.13 Distributed cache
08:52

4.14 Counters
16:43

4.15 MRUnit Test
06:52

4.16 Multiple inputs
12:22

4.17 ReadSequence File
07:43
+
Module 5- Apache Pig
21 Lectures 02:39:26
5.1 Introduction to Apache Pig
01:36

5.2 PIG Latin language
03:52

5.3 Running PIG in Different Modes
02:49

5.4 Apache PIG Architecture
01:18

5.5 Grunt Shell
07:17

5.6 Pig Latin Statements
07:22

5.7 Pig Data Model- Scalar Types
06:09

5.8 Complex Types
10:49

5.9 Arithmetic Operators
06:30

5.10 Comparison Operators
10:36

5.11 Cast Operator
12:44

5.12 Type Construction Operators
06:58

5.13 Relational Operators
00:45

5.14 Loading and Storing
05:37

5.15 Filtering Operators
10:39

5.16 Grouping and Joining Operator- Part 1
16:39

5.17 Grouping and Joining Operator- Part 2
13:40

5.18 Combining and Splitting Operators
09:30

5.19 Sorting Operators
06:48

5.20 Diagnostic Operators
10:58

5.21 Filtering Operators-Pig Streaming with Python
06:50
+
Module 6- Apache Hive
18 Lectures 03:15:49
6.1 What is Hive
01:19

6.2 Hive Use Case@ Twitter
02:46

6.3 Hive vs MapReduce
02:44

6.4 What is Hive
03:18

6.5 Advantages of HiveQL
02:50

6.6 Hive Architecture
04:53

6.7 Data Types in Hive
02:49

6.8 Hive Query Language
01:42

6.9 DDL on DataBase
13:56

6.10 DDL on Tables
11:53

6.11 Different Tables in Hive
18:39

6.12 Advanced DDL on Tables
21:54

6.13 File Format in Hive
06:10

6.14 DML- Loading Data into tables
11:50

6.15 Managing Output
24:41

6.16 HiveQL-Queries
08:14

6.17 Operators and Functions in Hive
25:40

6.18 Hive Clauses
30:31
+
Apache HBase: NoSQL Database for Hadoop
12 Lectures 45:46
7.1 What is HBase
01:28

7.2 HBase history
01:36

7.3 Building blocks of hbase
03:50

7.4 Column family in Hbase
03:09

7.5 Storage of Column Family
02:39

7.6 Data Model in HBase
03:31

7.7 Timestamp as Versions
03:04

7.8 Getting Started with HBase Shell
02:55

7.9 DDL in HBase part-1
06:12

7.10 DDL in HBase part-2
04:05

7.11 DDL in HBase part-3
04:41

7.12 DML in HBase
08:36
About the Instructor
Edionik Solutions
5.0 Average rating
1 Review
33 Students
2 Courses
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