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Big Data
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Course sections

Section 1
Module 1
1
PL-SQL (Procedure Language – SQL)
2
Introduction to Programming Languages
3
Introduction to PL/SQL
4
The Advantages of PL/SQL
5
PL/SQL Architecture
6
PL/SQL Data types
Section 2
During this course
Section 3
you will learn:
1
Introduction to Big Data and Analytics
2
Introduction to Hadoop
3
Hadoop Ecosystem – Concepts
4
Hadoop MapReduce Concepts and Features
5
Developing the MapReduce Applications
6
Pig Concepts
7
Hive Concepts
8
Sqoop Concepts
9
Flume Concepts
10
Oozie Workflow Concepts
11
Impala Concepts
12
Hue Concepts
13
HBASE Concepts
14
ZooKeeper Concepts
15
Real Life Use Cases
Section 4
Reporting Tool
1
Tableau
Section 5
Virtualbox/VM Ware
1
Basics
2
Installations
3
Backups
4
Snapshots
Section 6
Linux
1
Basics
2
Installations
3
Commands
Section 7
Hadoop
1
Why Hadoop?
2
Scaling
3
Distributed Framework
4
Hadoop v/s RDBMS
5
Brief History of Hadoop
Section 8
Setup hadoop
1
Pseudo Mode
2
Cluster Mode
3
Ipv6
4
Ssh
5
Installation of Java, Hadoop
6
Configurations of Hadoop
7
Hadoop Processes ( NN, SNN, JT, DN, TT)
8
Temporary Directory
9
Common Errors when running Hadoop Cluster, Solutions
Section 9
HDFS- Hadoop Distributed File System
1
HDFS Design and Architecture
2
HDFS Concepts
3
Interacting HDFS using Command-Line
4
Interacting HDFS using Java APIs
5
Dataflow
6
Blocks
7
Replica
Section 10
Hadoop Processes
1
Name Node
2
Secondary Name Node
3
Job Tracker
4
Task Tracker
5
Data Node
Section 11
MapReduce
1
Developing MapReduce Application
2
Phases in MapReduce Framework
3
Map Reduce Input and Output Formats
4
Advanced Concepts
5
Sample Applications
6
Combiner
Section 12
Joining datasets in MapReduce jobs
1
Map-side Join
2
Reduce-side Join
Section 13
MapReduce – Customization
1
Custom Input Format Class
2
Hash Partitioner
3
Custom Partitioner
4
Sorting Techniques
5
Custom Output Format Class
Section 14
HADOOP PROGRAMMING LANGUAGES
1
HIVE
2
PIG
Section 15
OVERVIEW – HADOOP DEVELOPER
Section 16
Introduction – The Motivation for Hadoop
1
Problems with Traditional Large-scale Systems
2
Requirements for a New Approach
Section 17
Hadoop: Basic Concepts
1
An Overview of Hadoop
2
The Hadoop Distributed File System
3
Hands-On Exercise
4
How MapReduce Works
5
Hands-On Exercise
6
Anatomy of a Hadoop Cluster
7
Other Hadoop Ecosystem Components
Section 18
Writing a MapReduce Program
1
The MapReduce Flow
2
Examining a Sample MapReduce Program
3
Basic MapReduce API Concepts
4
The Driver Code
5
The Mapper
6
The Reducer
7
Hadoop’s Streaming API
8
Using Eclipse for Rapid Development
9
Hands-on Exercise
10
The New MapReduce API
Section 19
Common Map Reduce Algorithms
1
Sorting and Searching
2
Indexing
3
Machine Learning With Mahout
4
Term Frequency – Inverse Document Frequency
5
Word Co-occurrence
6
Hands-On Exercise
Section 20
PIG Concepts..
1
Data Loading in PIG
2
Data Extraction in PIG
3
Data Transformation in PIG
4
Hands-on Exercise on PIG
Section 21
Hive Concepts
1
Hive Query Language
2
Alter and Delete in Hive
3
Partition in Hive
4
Indexing
5
Joins in Hive.Unions in hive
6
Industry Specific Configuration of Hive Parameters
7
Authentication
8
Statistics with Hive
9
Archiving in Hive
10
Hands-on exercise
Section 22
Working with Sqoop
1
Introduction
2
Import Data
3
Export Data
4
Sqoop Syntax
5
Databases Connection
6
Hands-on exercise
Section 23
Working with Flume
1
Introduction
2
Configuration and Setup
3
Flume Sink with Example
4
Channel
5
Flume Source with Example
6
Complex Flume Architecture
Section 24
OOZIE
1
OOZIE Concepts
Section 25
IMPALA
1
IMPALA Concepts
Section 26
HUE
1
HUE Concepts
Section 27
HBASE
1
HBASE Concepts
Section 28
Zookeepers
1
Zookeepers concepts
Section 29
Reporting Tool
1
Reporting Tool

Common Map Reduce Algorithms

Term Frequency – Inverse Document Frequency

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Common Map Reduce Algorithms Machine Learning With Mahout
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Common Map Reduce Algorithms Word Co-occurrence
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