This document discusses big data, including key enablers like increased storage and processing power. It notes that 90% of data today was created in the last two years. Big data comes from sources like mobile devices, sensors, and social media. The challenge is managing and analyzing large amounts of diverse data in a timely way. Common big data types include structured, unstructured, semi-structured, text, graph, and streaming data. Big data analytics can provide value across many domains. Issues include privacy, regulation, and ensuring analysis solves meaningful problems. The big data industry is large and growing rapidly.

1. Big Data
By-
Raghav Tripathi
Department of Computer
Science Engineering,
MMMUT

2.  Key enablers for the appearance and growth of ‘Big-Data’ are:
◦ Increase in storage capabilities
◦ Increase in processing power
◦ Availability of data
◦ Every day we create 2.5 quintillion bytes of data; 90% of the
data in the world today has been created in the last two
years alone

3. REF:2

4. Mobile Devices
Readers/Scanners
Science facilities
Microphones
Cameras
Social Media
Programs/ Software

5. Social media and networks
(all of us are generating data)
Scientific instruments
(collecting all sorts of data)
Mobile devices
(tracking all objects all the tim
Sensor technology and
networks
(measuring all kinds of data)
 The progress and innovation is no longer hindered by the ability to collect data
 But, by the ability to manage, analyze, summarize, visualize, and discover
knowledge from the collected data in a timely manner and in a scalable fashion.
 Activity data, Conversation data, Sensor data and phota and video image data.
5

6.  Relational Data (Tables/Transaction/Legacy
Data)
 Text Data (Web)
 Semi-structured Data (XML)
 Graph Data
◦ Social Network, Semantic Web (RDF), …
 Streaming Data
◦ You can only scan the data once

7.  Aggregation and Statistics
◦ Data warehouse and OLAP
 Indexing, Searching, and Querying
◦ Keyword based search
◦ Pattern matching (XML/RDF)
 Knowledge discovery
◦ Data Mining
◦ Statistical Modeling

8. • Main Frame
• SQL Server
• Oracle
• DB2
• Sybase
• Access , Excel,
txt etc.
• Teradata
• Emerging Market
Data
• E-commerce
• Third Party Data
• Weather
• Stock
Exchange
• Syndicated Data
• Social Media
• Chats
• Blogs
• Tweets
• Likes
• Followers
• Digital , Video
• Audio
• Geo- Spacial
Structured Un-Structured Semi-
Structured

9. Every minute
we send 204 million emails, generate 1,8 million Facebook likes, send 278
thousand Tweets, and up-load 200,000 photos to Facebook.

12. Disk Speed :
• Traditional Hard Drive: 60-100 mbps
• Solid State Disk : 250-500 mbps
Processing Time :
For 1 TB of File :
Traditional Hard-Disk
Solid State Disk
So, Main problem (storage and analysis) : Disk speed is increasing almost
linearly whereas BIG DATA is growing Exponentially!!
Other Problems:
Risk of Machine Failure , Backup Problem , Expensive .
10000 seconds
167 minutes~3
hrs
2000 Seconds
Approx 33 mins.

13. The‘Datafication’ of
our World;
• Activities
• Conversations
• Words
• Voice
• Social Media
• Browser logs
• Photos
• Videos
• Sensors
• Etc.
Volume
Veracity
Variety
Velocity
Analysing
Big Data:
• Text
analytics
• Sentiment
analysis
• Face
recognition
• Voice
analytics
• Movement
analytics
• Etc.
Value
Turning Big Data into Value:

14.  MapReduce computation framework
 Hadoop
 Distributed Database
 NoSQL technologies

16. A Application Of Big Data analytics
Homeland
Security
Smarter
Healthcare
Multi-channel
sales
Telecom
Manufacturing
Traffic Control
Trading
Analytics
Search
Quality

18. • Will be so overwhelmed
• Need the right people and solve the right problems
• Costs escalate too fast
• Isn’t necessary to capture 100%
• Many sources of big data
is privacy
• self-regulation
• Legal regulation
18

19.  $15 billion on software firms only specializing in data
management and analytics.
 This industry on its own is worth more than $100 billion and
growing at almost 10% a year which is roughly twice as fast
as the software business as a whole.
 In February 2012, the open source analyst firm Wikibon
released the first market forecast for Big Data , listing $5.1B
revenue in 2012 with growth to $53.4B in 2017
 The McKinsey Global Institute estimates that data volume is
growing 40% per year, and will grow 44x between 2009 and
2020.

20.  Silicon valley and through social media is
making Big Data a global phenom.
 Not only Big Data is “cool” it happens to be a
huge growth area as well.