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

During this course

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

Reporting Tool

1
Tableau

Virtualbox/VM Ware

1
Basics
2
Installations
3
Backups
4
Snapshots

Linux

1
Basics
2
Installations
3
Commands

Hadoop

1
Why Hadoop?
2
Scaling
3
Distributed Framework
4
Hadoop v/s RDBMS
5
Brief History of Hadoop

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

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

Hadoop Processes

1
Name Node
2
Secondary Name Node
3
Job Tracker
4
Task Tracker
5
Data Node

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

Joining datasets in MapReduce jobs

1
Map-side Join
2
Reduce-side Join

MapReduce – Customization

1
Custom Input Format Class
2
Hash Partitioner
3
Custom Partitioner
4
Sorting Techniques
5
Custom Output Format Class

HADOOP PROGRAMMING LANGUAGES

1
HIVE
  • Introduction
  • Installation and Configuration
  • Interacting HDFS using HIVE
  • MapReduce Programs through HIVE
  • HIVE Commands
  • Loading, Filtering, Grouping
  • Data Types, Operators
  • Joins, Groups
  • Sample programs in HIVE
2
PIG
  • Basics
  • Installation and Configurations
  • Commands

OVERVIEW – HADOOP DEVELOPER

Introduction – The Motivation for Hadoop

1
Problems with Traditional Large-scale Systems
2
Requirements for a New Approach

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

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

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

PIG Concepts..

1
Data Loading in PIG
2
Data Extraction in PIG
3
Data Transformation in PIG
4
Hands-on Exercise on PIG

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

Working with Sqoop

1
Introduction
2
Import Data
3
Export Data
4
Sqoop Syntax
5
Databases Connection
6
Hands-on exercise

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

OOZIE

1
OOZIE Concepts

IMPALA

1
IMPALA Concepts

HUE

1
HUE Concepts

HBASE

1
HBASE Concepts

Zookeepers

1
Zookeepers concepts

Reporting Tool

1
Reporting Tool

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Big Data Course Classes In Fremont CA
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Working hours

Monday9:30 am - 6.00 pm
Tuesday9:30 am - 6.00 pm
Wednesday9:30 am - 6.00 pm
Thursday9:30 am - 6.00 pm
Friday9:30 am - 5.00 pm
SaturdayClosed
SundayClosed

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