concept of big data

2. Outline
 Introduction
 What is Big Data
 Generators of Big Data
 Characteristic of Big Data
 Benefit of Big data
 Hadoop
 Hadoop components
 Big Data vs. Hadoop

3. Introduction

5. What is Big data
 Is very large data sets that may be analyzed computationally to
reveal patterns, trends, and associations, especially relating to
Customers behavior and interactions.
 A technology term about Data that becomes too large to be
managed in a manner that is previously known to work
normally.

6. Big Data generators
This data comes from everywhere:
sensors used to gather climate information,
posts to social media sites,
digital pictures
online Shopping
Airlines , and many more…
This data is “ big data.”

7. Characteristic Of Big Data
“Big data is the data characterized by 3 attributes: Volume, Velocity and Variety .”

8. Volume
 It is the size of the data which determines the value and potential of the data under
consideration. The name ‘Big Data’ itself contains a term which is related to size
and hence the characteristic.

9. Variety
 Data today comes in all types of formats. Structured, numeric data in traditional
databases. Unstructured text documents, email, stock ticker data and financial
transactions and semi-structured data too.

10. Velocity
 speed of generation of data or how fast the data is generated and processed to meet
the demands and the challenges which lie ahead in the path of growth and
development.

11.  FB generates 100TB daily
 Twitter generates 8TB of data Daily

12. Benefit of Big data
 Cost Reduction from Big Data Technologies
 Time Reduction from Big Data
 Developing New Big Data-Based Offerings
 Supporting Internal Business Decisions
 Real-time big data isn’t just a process for storing petabytes or Exabyte's of
data in a data warehouse, It’s about the ability to make better decisions and
take meaningful actions at the right time.

14. Advantages and disadvantages of big data

15. What is Hadoop
 Flexible and available architecture for large scale computation and data
performance on a network of commodity hardware
 Framework that allows for distributed processing of large data sets across
clusters of commodity servers
 – Store large amount of data
 – Process the large amount of data stored
 Getting result from HDFS

16. Hadoop Components
 Hadoop Distributed File system (HDFS)
 Map Reduce
 Name Node
 Data Node
 Pig , hive

20. Map reduces
MapReduceisaprogrammingmodelforprocessinglargedatasetswithaparallel,distributed
algorithmonacluster
 Scale-outArchitecture-Addserverstoincreaseprocessingpower
 Security&Authentication-WorkswithHDFSsecuritytomakesurethatonlyapprovedusers
canoperateagainstthedatainthesystem
 ResourceManager-Employsdatalocalityandserverresourcestodetermineoptimalcomputing
operations
 OptimizedScheduling-Completesjobsaccordingtoprioritization
 Flexibility-Procedurescanbewritteninvirtuallyanyprogramminglanguage
 Resiliency&HighAvailability-Multiplejobandtasktrackersensurethatjobsfail
independently andrestartautomatically

23. BI VS Big Data