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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
Instructor:
Edionik Solutions
290 students enrolled
English [Auto-generated]
Basics of Big Data
Detailed understanding of Big Data analytics
Master HDFS, MapReduce, Hive, Pig, HBase, Yarn

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

Module 1:- Introduction to Big Data and Hadoop

1
1.1 Introduction to BigData
2
1.2 Types of Data
3
1.3 Introduction to Hadoop
4
1.4 Comparison with RDBMS
5
1.5 Hadoop Features
6
1.6 Hadoop Ecosystem
7
1.7 Hadoop Core Components

Module 2- HDFS(Hadoop Distributed File System)

1
2.1 Hadoop Distributed File System
2
2.2 HDFS Files and Blocks
3
2.3 HDFS Components and Architecture
4
2.4 HDFS File Read-Write
5
2.5 HDFS Commands

Module 3- Mapreduce

1
3.1 Mapreduce
2
3.2 Map-Reduce Operation
3
3.3 Map-reduce Example
4
3.4 HDFS Input Splits
5
3.5 MapReduce Architecture
6
3.6 Combiners and Partitioners
7
3.7 MapReduce Data Flow
8
3.8 MapReduce Examples

Module 4- Advanced Mapreduce-I

1
4.1 Advanced MapReduce I
2
4.2 GenericOptionsParser, Tool and ToolRunner
3
4.3 Serialization and Deserialisation
4
4.4 Chaining of Jobs
5
4.5 Distributed Cache
6
4.6 Counters
7
4.7 JUnit Testing
8
4.8 Schedulers
9
4.9 Data Compression in Hadoop
10
4.10 Different Input and Output Formats in MapReduce
11
4.11 Chain Mapping
12
4.12 Compression in Gzip
13
4.13 Distributed cache
14
4.14 Counters
15
4.15 MRUnit Test
16
4.16 Multiple inputs
17
4.17 ReadSequence File

Module 5- Apache Pig

1
5.1 Introduction to Apache Pig
2
5.2 PIG Latin language
3
5.3 Running PIG in Different Modes
4
5.4 Apache PIG Architecture
5
5.5 Grunt Shell
6
5.6 Pig Latin Statements
7
5.7 Pig Data Model- Scalar Types
8
5.8 Complex Types
9
5.9 Arithmetic Operators
10
5.10 Comparison Operators
11
5.11 Cast Operator
12
5.12 Type Construction Operators
13
5.13 Relational Operators
14
5.14 Loading and Storing
15
5.15 Filtering Operators
16
5.16 Grouping and Joining Operator- Part 1
17
5.17 Grouping and Joining Operator- Part 2
18
5.18 Combining and Splitting Operators
19
5.19 Sorting Operators
20
5.20 Diagnostic Operators
21
5.21 Filtering Operators-Pig Streaming with Python

Module 6- Apache Hive

1
6.1 What is Hive
2
6.2 Hive Use Case@ Twitter
3
6.3 Hive vs MapReduce
4
6.4 What is Hive
5
6.5 Advantages of HiveQL
6
6.6 Hive Architecture
7
6.7 Data Types in Hive
8
6.8 Hive Query Language
9
6.9 DDL on DataBase
10
6.10 DDL on Tables
11
6.11 Different Tables in Hive
12
6.12 Advanced DDL on Tables
13
6.13 File Format in Hive
14
6.14 DML- Loading Data into tables
15
6.15 Managing Output
16
6.16 HiveQL-Queries
17
6.17 Operators and Functions in Hive
18
6.18 Hive Clauses

Apache HBase: NoSQL Database for Hadoop

1
7.1 What is HBase
2
7.2 HBase history
3
7.3 Building blocks of hbase
4
7.4 Column family in Hbase
5
7.5 Storage of Column Family
6
7.6 Data Model in HBase
7
7.7 Timestamp as Versions
8
7.8 Getting Started with HBase Shell
9
7.9 DDL in HBase part-1
10
7.10 DDL in HBase part-2
11
7.11 DDL in HBase part-3
12
7.12 DML in HBase
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11 hours on-demand video
Full lifetime access
Access on mobile and TV
Certificate of Completion