Big data refers to large, complex data sets that are difficult to analyze using traditional data processing applications. It is characterized by high volume, velocity, and variety of data that require new forms of processing to enable enhanced decision making, insights, and process automation. The challenges with big data include extracting useful information and insights from large amounts of noisy, unstructured data generated at high speeds from a variety of sources. Developers must learn to identify meaningful signals and patterns in data despite the presence of noise. As more human activities and conversations become digitized, there are opportunities to gain unexpected insights by monitoring and analyzing human data at large scales. However, organizations must first understand their existing data before attempting to extract value from it at large scales.

1. Wednesday, May 20, 2015 Big Data By Sathish Yellanki Slide No : 1
A Very Big Question To Answer

2. What is Big Data?

3. Formal Definitions of Big Data

4. Big Data is The Frontier of A Firm's Ability To
Store, Process, Access (SPA)
All The Data it Needs To
Operate Effectively
Make Decisions
Reduce Risks
Serve Customers
Definition 01
…Forrester

5. Big Data in General is Defined As
High Volume, Velocity And Variety
Information Assets That Demands
Cost-Effective
Innovative Forms of Information Processing For
Enhanced Insight And Decision Making
Definition 02
…Gartner

6. Big Data is Data That Exceeds The
Processing Capacity of Conventional Database
Systems
The Data is Too Big, Moves Too Fast, OR Doesn't Fit
The Structures of Clients Database Architectures
To Gain Value From This Data, The Client Must
Choose An Alternative Way To Process it
Definition 03
…O’Reilly

7. Big Data is The Data Characterized By 3 Attributes
Volume
Variety
Velocity
Definition 04
…IBM

8. Big Data is Data Characterized By 4 Key Attributes
Volume
Variety
Velocity
Value
Definition 05
…Oracle

9. How An Analyst, Designer, Administrator,
Architect And Developer Should See The Big Data

10. By Complexity of Storage Size
By Criticality of Backup And Recovery
By Challenge of Search And Optimizing Time
By Variations of The Tools And Technologies
By The Dynamism of Change And Scalability
By The Resistance in Building The Man Power
By The Speed of Research in Applying
By The Variety of Data Formats That Arise
By The Composition of Demanded Multiple Skillsets

11. How To Build An Idea Practically About Big Data?

12. Question
What is The Size That We Need To Fit A Single
Character of Data?
Answer
One Byte
Situational Question
Do You Have The Required Memory Size?
Scenarios That May Arise
• Yes, Then it is Small Data
• No, Then it is Big Data As Per Your Requirement
What To Do To Fit The Data When it is “NO”
• Design And Develop Architecture For Big Data

13. Question
What is The Size That We Need To Fit A Single Digit
of a Number?
Answer
Two Bytes
Situational Question
Do You Have The Required Memory Size?
Scenarios That May Arise
• Yes, Then it is Small Data
• No, Then it is Big Data As Per Your Requirement
What To Do To Fit The Data When it is “NO”
• Design And Develop Architecture For Big Data

14. Question
What is The Maximum Size of Data Storage in A
MS-Word File?
Answer
32 MB
Situational Question
Do You Have Your Data Upto OR More Than 32 MB
Scenarios That May Arise
• Yes, Then it is Small Data
• No, Then it is Big Data As Per Your Requirement
What To Do To Fit The Data When it is “NO”
• Design And Develop Architecture For Big Data

15. Question
What is The Maximum Size of Data Storage in A
Data File of Oracle?
Answer
Block Size Maximum Datafile File Size
2K 4194303 * 2K = 8 GB
4K 4194303 * 4K = 16 GB
8K 4194303 * 8K = 32 GB
16K 4194303 * 16K = 64 GB
32K 4194303 * 32K = 128 GB
Block Size Maximum Datafile File Size
2k 4294967295 * 2K = 8 TB
4k 4294967295 * 4K = 16 TB
8k 4294967295 * 8K = 32 TB
16k 4294967295 * 16K = 64 TB
32k 4294967295 * 32K = 128 TB
Big File Table Space
Normal File Table Space

16. Finally How To Identify The “Big Data” Limit?

17. • Big Data is Something When it is Unable To Fit
into The RAM.
• Small Data is Something When it Can Fit into
The RAM.

18. Let Us Have A Symbolic Reference For Clarity

19. Byte : One Grain of Rice

20. Byte : One Grain of Rice
Kilobyte : Cup of Rice

21. Byte : One Grain of Rice
Kilobyte : Cup of Rice
Megabyte : 8 Bags of Rice

22. Byte : One Grain of Rice
Kilobyte : Cup of Rice
Megabyte : 8 Bags of Rice
Gigabyte : 3 Semi Trucks

23. Byte : One Grain of Rice
Kilobyte : Cup of Rice
Megabyte : 8 Bags of Rice
Gigabyte : 3 Semi Trucks
Terabyte : 2 Container Ships

24. Byte : One Grain of Rice
Kilobyte : Cup of Rice
Megabyte : 8 Bags of Rice
Gigabyte : 3 Semi Trucks
Terabyte : 2 Container Ships
Petabyte : Blankets Manhattan
Area
• Total 33.6 SQ MI (87 KM
2
)
• Land 22.8 SQ MI (59 KM
2
)
• Water 10.8 SQ MI (28 KM
2
) 32%
Population (2013)
• Total 1,626,159
• Density 70,825.6/SQ MI (27,345.9/KM
2
)

25. Byte : One Grain of Rice
Kilobyte : Cup of Rice
Megabyte : 8 Bags of Rice
Gigabyte : 3 Semi Trucks
Terabyte : 2 Container Ships
Petabyte : Blankets Manhattan
Exabyte : Blankets West Coast States

26. Byte : One Grain of Rice
Kilobyte : Cup of Rice
Megabyte : 8 Bags of Rice
Gigabyte : 3 Semi Trucks
Terabyte : 2 Container Ships
Petabyte : Blankets Manhattan
Exabyte : Blankets West Coast States
Zettabyte : Fills The Pacific Ocean
Area of 165,200,000 KM²

27. Byte : One Grain of Rice
Kilobyte : Cup of Rice
Megabyte : 8 Bags of Rice
Gigabyte : 3 Semi Trucks
Terabyte : 2 Container Ships
Petabyte : Blankets Manhattan
Exabyte : Blankets West Coast States
Zettabyte : Fills The Pacific Ocean
Yottabyte : A Earth Size Rice Ball!
Surface Area : 510,072,000 KM²
Radius : 6,371 KM
Volume : 1.08321×1012 KM3

28. Area of Application By Memory Size

29. Byte : One Grain of Rice
Kilobyte : Cup of Rice
Megabyte : 8 Bags of Rice
Gigabyte : 3 Semi Trucks
Terabyte : 2 Container Ships
Petabyte : Blankets Manhattan
Exabyte : Blankets West Coast States
Zettabyte : Fills The Pacific Ocean
Yottabyte : A Earth Size Rice Ball!
Hobbyist

30. Byte : One Grain of Rice
Kilobyte : Cup of Rice
Megabyte : 8 Bags of Rice
Gigabyte : 3 Semi Trucks
Terabyte : 2 Container Ships
Petabyte : Blankets Manhattan
Exabyte : Blankets West Coast States
Zettabyte : Fills The Pacific Ocean
Yottabyte : A Earth Size Rice Ball!
Hobbyist
Desktop

31. Byte : One Grain of Rice
Kilobyte : Cup of Rice
Megabyte : 8 Bags of Rice
Gigabyte : 3 Semi Trucks
Terabyte : 2 Container Ships
Petabyte : Blankets Manhattan
Exabyte : Blankets West Coast States
Zettabyte : Fills The Pacific Ocean
Yottabyte : A Earth Size Rice Ball!
Hobbyist
Desktop
Internet

32. Byte : One Grain of Rice
Kilobyte : Cup of Rice
Megabyte : 8 Bags of Rice
Gigabyte : 3 Semi Trucks
Terabyte : 2 Container Ships
Petabyte : Blankets Manhattan
Exabyte : Blankets West Coast States
Zettabyte : Fills The Pacific Ocean
Yottabyte : A Earth Size Rice Ball!
Big Data

33. Byte : One Grain of Rice
Kilobyte : Cup of Rice
Megabyte : 8 Bags of Rice
Gigabyte : 3 Semi Trucks
Terabyte : 2 Container Ships
Petabyte : Blankets Manhattan
Exabyte : Blankets West Coast States
Zettabyte : Fills The Pacific Ocean
Yottabyte : A Earth Size Rice Ball!
Hobbyist
Desktop
Internet
Big Data
Who Knows?

34. What is The Key Parameter To Be Considered in
Big Data?

35. • Big Data is Not About The
Size of The Data.
• Big Data is About The
Value Within The Data.

36. So, What is The Level of value?

37. When Big Data Will Be Valued?

38. Market Intelligence?

39. Market Intelligence?
Business Intelligence?

40. Market Intelligence?
Business Intelligence?
Secret Intelligence?

41. Market Intelligence?
Business Intelligence?
Secret Intelligence?
Forensic Intelligence?

42. Market Intelligence?
Business Intelligence?
Secret Intelligence?
Forensic Intelligence?
Medical Intelligence?

43. Market Intelligence?
Business Intelligence?
Secret Intelligence?
Forensic Intelligence?
Space Intelligence?
Medical Intelligence?

44. Big Data

45. Wednesday, May 20, 2015 Big Data By Sathish Yellanki Slide No : 45
A Big Question For The Next Generation

46. Answer
Yes, Our Society And System is Leaving Behind A
Digital Footprint.
Wednesday, May 20, 2015 Big Data By Sathish Yellanki Slide No : 46
Question
Are We Going To Be A Digital World?

47. Wednesday, May 20, 2015 Big Data By Sathish Yellanki Slide No : 47
A Big Question For The Community of Humans

48. Wednesday, May 20, 2015 Big Data By Sathish Yellanki Slide No : 48
Answer
Majority of The Human Race is Living Online.
Question
Where Are We Living And Spending Time Today?
We All Are Digitally And Virtually Expressing Our

49. Wednesday, May 20, 2015 Big Data By Sathish Yellanki Slide No : 49
Answer
Majority of The Human Race is Living Online.
Question
Where Are We Living And Spending Time Today?
We All Are Digitally And Virtually Expressing Our
• Attitudes

50. Wednesday, May 20, 2015 Big Data By Sathish Yellanki Slide No : 50
Answer
Majority of The Human Race is Living Online.
Question
Where Are We Living And Spending Time Today?
We All Are Digitally And Virtually Expressing Our
• Attitudes
• Likes And Dislikes

51. Wednesday, May 20, 2015 Big Data By Sathish Yellanki Slide No : 51
Answer
Majority of The Human Race is Living Online.
Question
Where Are We Living And Spending Time Today?
We All Are Digitally And Virtually Expressing Our
• Attitudes
• Likes And Dislikes
• Our Opinions

52. Wednesday, May 20, 2015 Big Data By Sathish Yellanki Slide No : 52
Answer
Majority of The Human Race is Living Online.
Question
Where Are We Living And Spending Time Today?
We All Are Digitally And Virtually Expressing Our
• Attitudes
• Likes And Dislikes
• Our Opinions
• Perspectives

53. Wednesday, May 20, 2015 Big Data By Sathish Yellanki Slide No : 53
Answer
Majority of The Human Race is Living Online.
Question
Where Are We Living And Spending Time Today?
We All Are Digitally And Virtually Expressing Our
• Attitudes
• Likes And Dislikes
• Our Opinions
• Perspectives
• Happiness And Sorrow

54. Wednesday, May 20, 2015 Big Data By Sathish Yellanki Slide No : 54
Answer
Majority of The Human Race is Living Online.
Question
Where Are We Living And Spending Time Today?
We All Are Digitally And Virtually Expressing Our
• Attitudes
• Likes And Dislikes
• Our Opinions
• Perspectives
• Happiness And Sorrow
• Responsibilities And Rights

55. Wednesday, May 20, 2015 Big Data By Sathish Yellanki Slide No : 55
Answer
Majority of The Human Race is Living Online.
Question
Where Are We Living And Spending Time Today?
We All Are Digitally And Virtually Expressing Our
• Attitudes
• Likes And Dislikes
• Our Opinions
• Perspectives
• Happiness And Sorrow
• Responsibilities And Rights
• Governance

56. Wednesday, May 20, 2015 Big Data By Sathish Yellanki Slide No : 56
Answer
Majority of The Human Race is Living Online.
Question
Where Are We Living And Spending Time Today?
We All Are Digitally And Virtually Expressing Our
• Attitudes
• Likes And Dislikes
• Our Opinions
• Perspectives
• Governance
• Happiness And Sorrow
• Responsibilities And Rights
• Strategies And Standards

57. Wednesday, May 20, 2015 Big Data By Sathish Yellanki Slide No : 57
Answer
Majority of The Human Race is Living Online.
Question
Where Are We Living And Spending Time Today?
We All Are Digitally And Virtually Expressing Our
• Attitudes
• Likes And Dislikes
• Our Opinions
• Perspectives
• Happiness And Sorrow
• Responsibilities And Rights
• Governance
• Strategies And Standards
• Intelligence And Implementation

58. Wednesday, May 20, 2015 Big Data By Sathish Yellanki Slide No : 58
Answer
Majority of The Human Race is Living Online.
Question
Where Are We Living And Spending Time Today?
We All Are Digitally And Virtually Expressing Our
• Attitudes
• Likes And Dislikes
• Our Opinions
• Perspectives
• Happiness And Sorrow
• Responsibilities And Rights
• Governance
• Strategies And Standards
• Intelligence And Implementation
• Research And Application

59. Wednesday, May 20, 2015 Big Data By Sathish Yellanki Slide No : 59
A Big Question For The System Developers

60. Wednesday, May 20, 2015 Big Data By Sathish Yellanki Slide No : 60
Answer
Yes
Question
Are We Generating Huge Amounts of Data?

61. Wednesday, May 20, 2015 Big Data By Sathish Yellanki Slide No : 61
Question
What This Data is Actually Containing?
Answer
Data Integrated With A Lot of Information.
Information in Association With A Lot of Noise.

62. Wednesday, May 20, 2015 Big Data By Sathish Yellanki Slide No : 62
Question
What This Data is Actually Containing?
Answer
Data Integrated With A Lot of Information.
Information in Association With A Lot of Noise.
Data
IntelligentSystem

63. Wednesday, May 20, 2015 Big Data By Sathish Yellanki Slide No : 63
Answer
A Developer Should Develop The Ability To Hear
The Signal From The Noise.
Catching The Exact Signal For The Analysis And
Research is Considered As The Key.
Question
What Ability The Developers Should Develop?
Intelligent
System

64. Wednesday, May 20, 2015 Big Data By Sathish Yellanki Slide No : 64
Are We Aware of The Big Challenge That is Ahead

65. Wednesday, May 20, 2015 Big Data By Sathish Yellanki Slide No : 65
Question
What is The Challenge We Will Face?
Answer
Unlock The Human Conversation That is Taking
Place Around Us.

66. Wednesday, May 20, 2015 Big Data By Sathish Yellanki Slide No : 66
Question
What Are The Other Conversations We Should
Monitor?
Answer
• The Human Machine Interaction And
Interdependency
• The Ability To Make Machines More Expertise
in Managing The Failures OR if Possible Avoid
The Failure
• The Ability To Achieve The Super User
Friendliness Architectures
• Achieve Systems That Can Operate As Per The
Thought of The Humans

67. Wednesday, May 20, 2015 Big Data By Sathish Yellanki Slide No : 67
What Happens if We Can Listen To The Conversation
of The Humans Around?

68. Wednesday, May 20, 2015 Big Data By Sathish Yellanki Slide No : 68
• Move Towards The Unexpected Discoveries
• Re-Invent The Failed Prophesies in Science And
Technology
• Analyze The Systems With More Deeper In-
Sight
• Cross Question The Process For Greater Success
• Identify And Correct The Failures At The Early
Stages
• Report The System Break Ups At The Earliest
Than Expected
• Prevent a Total Failure of The System

69. Wednesday, May 20, 2015 Big Data By Sathish Yellanki Slide No : 69
The Biggest Question Finally on Big Data

70. Wednesday, May 20, 2015 Big Data By Sathish Yellanki Slide No : 70
Shall We Mine The Data OR Mind The Data?
Does The Organizations And People Know What
They Have To Do With Data What They Have With
Them?
No, Most People Do Not Know What To Do With
All The Data They Already Have…
• First We Should Mind The Data, Then Mine The
Data, Then Mind The Data Then Mine The Data,
Then Mind The Data Then Mine The Data…

71. Wednesday, May 20, 2015 Big Data By Sathish Yellanki Slide No : 71
So Finally Where To Start With Big Data?

72. Wednesday, May 20, 2015 Big Data By Sathish Yellanki Slide No : 72
Get Big
The Bulls Eye For Success Finally Lies in
By Starting
Small

73. Wednesday, May 20, 2015 Big Data By Sathish Yellanki Slide No : 73
Where I Should Focus For My Success?

74. Wednesday, May 20, 2015 Big Data By Sathish Yellanki Slide No : 74
Focus on The Areas That Contradict
With Impact of
Business
System
Process
Methodology

75. Wednesday, May 20, 2015 Big Data By Sathish Yellanki Slide No : 75
What is The Flow Chart of Big Data Analytics?

76. Wednesday, May 20, 2015 Big Data By Sathish Yellanki Slide No : 76
Start
Did it
Work?
Stop
Try Something Out of
The Business Data
YESNO
YES

77. Wednesday, May 20, 2015 Big Data By Sathish Yellanki Slide No : 77
Is Big Data is Really Big?

78. Wednesday, May 20, 2015 Big Data By Sathish Yellanki Slide No : 78
To Speak Frankly No
For The People Who Know How To Use
The Concept And Credits of The Big Data
Big Data is Not Big

79. Wednesday, May 20, 2015 Big Data By Sathish Yellanki Slide No : 79
What is The End Result if I Know How To Tame The
Big Data?

80. Wednesday, May 20, 2015 Big Data By Sathish Yellanki Slide No : 80
Big Data Simply Obliges All Your Needs
And
Looks And Executes Like Smart Data

81. Wednesday, May 20, 2015 Big Data By Sathish Yellanki Slide No : 81
Thank You See You Soon With Hadoop