*********TECHNO INDIA COLLEGE OF TECHNOLOGY**********
RAJARHAT,KOLKATA - 700156
A full powerpoint presentation on big data analytics and Hadoop. This is made by: SK IBRAHIM ANAM , SOUVIK JANA.
Designed by:
SK IBRAHIM ANAM
Group Members:
SOUVIK JANA.
SK IBRAHIM ANAM.
VISHAL KUMAR.
2. WHAT IS
Data is raw, unorganized facts that need to be
processed. Data can be something simple,
seemingly random and of itself worthless useless
until it is organized.
3. DIFFERENT TYPES OF DATA
Traditional RDBMS deals with
only Structured Data
Need of a Technology which deals with
Semi – Structured Data ,Unstructured
Data and Structured Data as well
Semi-Structured Data
4. Traditional Concept of Data Storage
Organizations
Banking Sector
Stock Exchange
Hospital
Social Media
Online Shopping
Others
Extract Data Transform Data
Load into
DataBase
End Users Generate
Reports & Perform
Analytics
Managing Data
Processing Data
Data GrowsDifficult
5. Drawback of Using Traditional Approach
Expensive Time Consuming Scalability
Storage Size Resource Failure
6. The Model of Generating or Consuming Data
Has Change...
OLD MODEL - Few companies are generating the data, all
other consuming the data.
NEW MODEL -All of us generating the data, and all of us
consuming the data.
8. WHAT IS
Big data means really a Big Data, it is a
collection of large datasets that cannot be
processed using traditional computing
techniques. It requires new architecture , new
techniques , various tools and frameworks .
11. WHERE THE BIG DATA IS USED
IT Industries
Manufacturing Industries
Telecommunications
Banking sector
Healthcare
12. CHALLENGES IN HANDLING BIG DATA
There are two main challenges in handle BIG DATA
1. How do we store and manage such a huge volume
of DATA, efficiently.
2. How do we process & extract valuable information
from the huge volume of DATA within a given
time frame.
14. WHAT IS
Hadoop is a open Source Framework. It is designed to
store and Process huge volume of Data, efficiently.
Hadoop is a platform that provides both distributed
storage and computational capabilities.
18. Features of Hadoop
Cost Effective System (Use Commodity Machine)
Large Cluster of Nodes (Processing Power & Storage
Capacity is Increase)
19. Features of Hadoop
Parallel Processing (Less Time is Required to Store &
Access the Data)
Distributed Data (Data is Distributed in Different Nodes)