Hadoop - Bigdata
Prerequisites
·
There are no pre-requisites
for this course.
·
Basic knowledge of Core Java
and SQL is advantageous.
Course Content
1.
Java
·
Overview of Java
·
Classes and Objects
·
Garbage Collection and Modifiers
·
Inheritance, Aggregation, Polymorphism
·
Command line argument
·
Abstract class and Interfaces
·
String Handling
·
Exception Handling, Multithreading
·
Serialization and Advanced Topics
·
Collection Framework, GUI, JDBC
2.
Linux
·
Unix History & Over View
·
Command line file-system browsing
·
Bash/CORN Shell
·
Users Groups and Permissions
·
VI Editor
·
Introduction to Process
·
Basic Networking
·
Shell Scripting live scenarios
3.
SQL
·
Introduction to SQL, Data Definition Language (DDL)
·
Data Manipulation Language(DML)
·
Operator and Sub Query
·
Various Clauses, SQL Key Words
·
Joins, Stored Procedures, Constraints, Triggers
·
Cursors /Loops / IF Else / Try Catch, Index
·
Data Manipulation Language (Advanced)
·
Constraints, Triggers,
·
Views, Index Advanced
Hadoop - Bigdata
1.
Introduction to Bigdata
·
Introduction and relevance
·
Uses of Big Data analytics in various industries like Telecom, E-
commerce, Finance and Insurance etc.
·
Problems with Traditional Large-Scale Systems
2.
Hadoop (Big Data) Ecosystem
·
Motivation for Hadoop
·
Different types of projects by Apache
·
Role of projects in the Hadoop Ecosystem
·
Key technology foundations required for Big Data
·
Limitations and Solutions of existing Data Analytics Architecture
·
Comparison of traditional data management systems with Big Data
management systems
·
Evaluate key framework requirements for Big Data analytics
·
Hadoop Ecosystem & Hadoop 2.x core components
·
Explain the relevance of real-time data
·
Explain how to use big and real-time data as a Business
planning tool
3.
Building Blocks
·
Quick tour of Java (As Hadoop is Written in Java , so it will help
us to understand it better)
·
Quick tour of Linux commands ( Basic Commands to traverse the Linux
OS)
·
Quick Tour of RDBMS Concepts (to use HIVE and Impala)
·
Quick hands on experience of SQL.
·
Introduction to Cloudera VM and usage instructions
4.
Hadoop Cluster Architecture – Configuration Files
·
Hadoop Master-Slave Architecture
·
The Hadoop Distributed File System - data storage
·
Explain different types of cluster setups (Fully distributed/Pseudo
etc.)
·
Hadoop Cluster set up - Installation
·
Hadoop 2.x Cluster Architecture
·
A Typical enterprise cluster – Hadoop Cluster Modes
5.
Hadoop Core Components – HDFS & Map Reduce (YARN)
6.
HDFS Overview & Data storage in HDFS
·
Get the data into Hadoop from local machine (Data Loading
Techniques) - vice versa
·
MapReduce Overview (Traditional way Vs. MapReduce way)
·
Concept of Mapper & Reducer
·
Understanding MapReduce program skeleton
·
Running MapReduce job in Command line/Eclipse
·
Develop MapReduce Program in JAVA
·
Develop MapReduce Program with the streaming API
·
Test and debug a MapReduce program in the design time
·
How Partitioners and Reducers Work Together
·
Writing Customer Partitioners Data Input and Output
·
Creating Custom Writable and Writable Comparable Implementations
7.
Data Integration Using Sqoop and
Flume
·
Integrating Hadoop into an existing Enterprise
·
Loading Data from an RDBMS into HDFS by Using Sqoop
·
Managing Real-Time Data Using Flume
·
Accessing HDFS from Legacy Systems with FuseDFS and HttpFS
·
Introduction to Talend (community system)