The document discusses big data, analytics, and their applications. It defines big data as large, complex datasets that are difficult to manage with traditional databases. Big data is characterized by its volume, velocity, and variety. Examples are given of how retailers, telecom companies, and e-retailers use big data analytics to gain insights. The document also outlines approaches to analytic development and discusses how various organizations use big data analytics in practice.

2. CONTENT:
 Introduction
 Why big data is required
 Big data
 Big data facts
 Big data 3 V’s
 Why big data is important
 Examples where big data is used
 Analytics
 Approach to analytic development
 Analysis of data through senser.
 Analytics can help in
 Big data analytics
 Big data analytics in practice
 How big data is used in twitter to get patterns
 Human resource cost and risk of big data.
 Big data analytics tools and technology
 Conclusions
 references

3. INTRODUCTION :
 OLTP: Online
Transaction Processing
(DBMSs)
 OLAP: Online Analytical
Processing (Data
Warehousing)
 RTAP: Real-Time
Analytics Processing
(Big Data Architecture &
technology)

4. WHY BIG DATA IS REQUIRED ?
 High availability of data.
 Increase in storage capabilities.
 Increase in processing power.

5. BIGDATA :
 Big data is a collection of data sets that are large
and complex in nature.
 They constitute both structured and unstructured
data that grow large so fast that they are not
manageable by traditional relational database
systems or conventional statistical tools.

6. BIG DATA FACTS:
 We create 2.5 quintillion bytes every day.
 90% of world’s data was created in the last 2 years
 80% of world’s data is unstructured.
 Facebook processes 500 TB per day and stores 30
petabytes of data.
 72 hrs of video are uploaded every minute.
 Twitter produces over 90 million tweets per day.

7. BIGDATA: 3 V’s
 Bigdata is usually transformed in three dimensions- volume, velocity and variety.
 Volume: Machine generated data is produced in larger quantities than non traditional
data.
 Velocity: This refers to the speed of data processing.
 Variety: This refers to large variety of input data which in turn generates large amount of
data as output.

8. EXAMPLES : 1. RETAILER COMPANY

11. 2. TELECOMMUNICATION

12. 3.E-RETAILER

13. WE CAN’T DEAL WITH SO MUCH
INFORMATION

14. WHAT IS ANALYTICS :

15. APPROACH TO ANALYTIC
DEVELOPMENT:
 Identify the data sources .
 Select the right tools and technology to collect ,store
and aggregate the data.
 Understanding the business domain.
 Build mathematical models for the analytics.
 Visualize.
 Validate your result.
 Learn ,adapt,and rebuild your analytic model.

16. ANALYSIS OF DATA THROUGH
SENSER:
 Senser data:
 A senser is a converter that measures a physical
quantity and transforms it into a digital signal .
 Sensers are always on , capturing data at a low
cost , and powering the “Internet of Things”

17. ANALYTICS CAN HELP IN :

18. WHAT IS BIG DATA ANALYTICS:
Big data analytics is a process of :
Collecting
Organizing and
Analyzing
of large sets of data (“ big data “) to
 discover patterns and
Other useful information

19. BIG DATA ANALYTICS IN PRACTICE :
Etihad airways uses
technology to harvest and
analyze gigabytes of data
generated by hundreds of
sensors working insides its
planes . This allows to
monitor planes in real time,
reduce fuel costs ,manage
plane maintenance , and
even spot problems before
they happen.

20. BIG DATA ANALYTICS IN PRACTICE
CONT’D:
Many people use
facebook to update
their status ,share
photos and “ like “
content.
The Obama
presidential compaign
used all that data on the
social network to not
just find voters but to
assemble an army of
volunteers.

21. BIG DATA ANALYTICS IN PRACTICE
CONT’D:
One of India’s highest -
rated TV shows
aggregates and analyzes
the millions of messages
it receives from viewers
on controversial issue
like female feticide ,
caste discrimination and
child abuse - and uses
that data to push for
political change.

22. Big data can detect bullying and
gain new social insights:
 The researchers have developed a machine learning
algorithm that’s identifying more than 15,000 tweets
per day relating to bullying .
 They developed their model by feeding it two sets of
tweets : one they had determined to be about bullying
activity and another that was not.
 Once the model learned the language identifiers of
tweets containing bullying, it started identifying a
huge amount of tweets from the Twitter firehouse
and it also discovered time patterns.

27. How Big data works :

28. Conclusion:
 The availability of big data ,low -cost commodity
hardware ,and new information management and
analytic software have produced a unique moment in
the history of data analysis.
 The convergence of these trends means that we have
the capabilities required to analyze astonishing data
sets quickly and cost – effectively for the first time in
history .
 These capabilities are neither theoretical nor trivial.

29. REFERENCES :
 https://www.youtube.com/watch?v=u5jWC89xBzI
 https://www.youtube.com/watch?v=jujE79yEu6Y
 http://labs.sogeti.com/big-data-can-detect-bullying-
and-gain-new-social-insights/