You can watch the replay for this Geek Sync webcast in the IDERA Resource Center: http://ow.ly/wOZP50A5cbL Expect a game changer at least once a decade. A technology that disrupts the status quo and ideally improves our lives in terms of efficiencies or general well-being. Blockchain is this decade’s game changer. We’ve past the hype cycle and now both startups and well-established organizations are focusing on practical uses of a replicated general ledger beyond Bitcoin. Join IDERA and Steve Hoberman as he walks through blockchain and its three levels of maturity. You will learn how blockchain will impact conceptual, logical, and physical data modeling. About Steve: Steve has trained more than 10,000 people in data modeling since 1992. Steve is known for his entertaining and interactive teaching style (watch out for flying candy!), and organizations around the globe have brought Steve in to teach his Data Modeling Master Class, which is recognized as the most comprehensive data modeling course in the industry. Steve is the author of nine books on data modeling, including the bestseller Data Modeling Made Simple. One of Steve’s frequent data modeling consulting assignments is to review data models using his Data Model Scorecard® technique. He is the founder of the Design Challenges group, Conference Chair of the Data Modeling Zone conference, recipient of the 2012 Data Administration Management Association (DAMA) International Professional Achievement Award, and highest rated tutorial presenter at Enterprise Data World 2014 and Enterprise Data World 2015.

1. Data Modeling and
Blockchain
Steve Hoberman
me@stevehoberman.com
www.SteveHoberman.com
www.TechnicsPub.com
www.DataModelingZone.com
June 21, 2017

2. Steve Hoberman
me@stevehoberman.com

4. Game Changer Every Decade
Mainframe
1970s 1980s 1990s 2000s 2010s

5. Game Changer Every Decade
PC
1970s 1980s 1990s 2000s 2010s

6. Game Changer Every Decade
Web
1970s 1980s 1990s 2000s 2010s

7. Game Changer Every Decade
Mobile
1970s 1980s 1990s 2000s 2010s

8. Game Changer Every Decade
Blockchain
1970s 1980s 1990s 2000s 2010s

9. Blockchain
1.Definition
2.1.0, 2.0, 3.0
3.Challenges
4.Data Modeling Impact

10. Ledger

11. Shared Ledger

12. Immutable Shared Ledger

13. A free
distributed
NoSQL
database that is
fault tolerant and
(almost) 100%
secure.
Blockchain (Developer Perspective)

14. Blockchain
(Modeler perspective)
Transaction
Transaction Public Key
Transaction Private Key (AK1:1)
Offering
Offering Public Key
Offering Private Key (AK1:1)
Party
Party Public Key
Party Private Key (AK1:1)
Purchase
Transaction Public Key (FK)
Offering Public Key (FK)
Be initiated by
Obtained through
Make

15. Blockchain
(Architect perspective)
Before

16. Blockchain
(Architect perspective)
After
D C D C
D C
D C

17. Blockchain
(Architect perspective)
F
F
F
F
F
F
F
F
F
F
F
F
DTC
Before (T+3)

18. Blockchain
(Architect perspective)
F
F
F
F
F
F
F
F
F
F
F
F
After (T + 10 minutes)
D C D C
D C
D C

19. Blockchain
(Architect perspective)
S
A
S
S
S
A
S
A
A
A
S
S
TP
Before (T+ up to 6 months)

20. Blockchain
(Architect perspective)
S
A
S
S
S
A
S
A
A
A
S
S
After (T+ 10 minutes)
D C D C
D C
D C

21. Blockchainopoly

22. By having multiple
“record keepers”,
we can trust that
the game is
played fairly,
without a central
authority.

23. Blockchain Levels (3 c’s)
1.0 (currency) 2.0
(contracts)
3.0 (claims)

24. Blockchain 1.0: Currency
• “Satoshi Nakamoto”
• Push not pull
• Micropayments
• Bitcoin has 90% of market
• What is Bitcoin, anyway?

25. Blockchain 2.0: Contracts
If-then statement

26. Blockchain 3.0: Claim
“I own this…”
“I did this…”

27. Top 5 Challenges
1. Performance
2. Evil
3. Government
4. Permanence
5. Usage

28. Blockchain on data modeling
• Stronger ties
– Data governance
– Data standards
– Data architecture
• Weaker ties
– RDBMS
• Questionable ties
– Agile
Blockchain
PDMLDMCDM
Data Model
Impact

29. Impact to CDM
• Industry scope
• Use cases
• Business glossary
• Architecture roadmap
Blockchain
PDMLDM
Data Model
CDM
Impact

30. Monopoly Conceptual
Structure
PropertyPlayer
Contain
Purchase

31. Impact to LDM
• Integration point (lineage)
• Greater importance to developers
• Some RDBMS artifacts disappear
• Naming standards critical
Blockchain
PDM
Data Model
CDM LDM
Impact

32. Player
Player Shape
Player First Name
Player Last Name
Property
Property Name
Player Shape (FK)
Property Cost
Property Color Name
Hotel
Property Name (FK)
Structure Sequence Number (FK)
House
Property Name (FK)
Structure Sequence Number (FK)
Structure
Structure Sequence Number
Property Name (FK)
Purchase
Contain
Monopoly Logical

33. Blockchain Data Model
CDM LDM PDM
Impact
Impact to Physical Data Modeling
• Additional metadata
– Private and public keys
– Number of recordkeepers
– % to make decisions
• Performance the main driver
– Performance vs. Availability
– Performance vs. Security
– Performance vs. Accuracy
• Same challenges as with any NoSQL DB

34. Need Blockchain here

35. Monopoly Physical
Z Z
Player
Player Public ID (PK)
Player Shape (AK1:1)
Player First Name
Player Last Name
Property
Property ID (PK)
Player Public ID (FK)
Property Name (AK1:1)
Property Cost
Property Color Name
Structure
Structure Public ID (PK)
Property ID (FK) (AK1:1)
Structure Sequence Number (AK1:2)
Hotel
Structure Public ID (PK)(FK)
House
Structure Public ID (PK)(FK)
Purchase
Contain

36. Monopoly Implementation

37. Blockchain
1.Definition
2.1.0, 2.0, 3.0
3.Challenges
4.Data Modeling Impact

38. Data Modeling and Blockchain
Steve Hoberman
me@stevehoberman.com
www.SteveHoberman.com
www.TechnicsPub.com
www.DataModelingZone.com