Philip Rathle, VP of Product at Neo4j, presents on the Connected Data Imperative at Neo4j GraphDay NYC

1. NEW YORK CITY
April 18, 2017
09:00-09:30
09:30-10:15
10:15-11:00
11:00-11:30
11:30-12:30
12:30-13:30
13:30-17:00
Breakfast and Registration
The Connected Data Imperative: Why
Graphs
Transform Your Data: A worked example
Break
Enterprise Ready: A Look at
Neo4j in Production
Lunch
Training Session
Agenda

2. Use of Graphs has created some of the most successful companies in the world
C
34,3%B
38,4%A
3,3%
D
3,8%
1,8%
1,8%
1,8%
1,8%
1,8%
E
8,1%
F
3,9%

4. The Connected Data Imperative: Neo4j in the Enterprise
SOFTWARE
FINANCIAL
SERVICES
RETAIL MEDIA & OTHER
SOCIAL
NETWORKS
TELECOM HEALTHCARE

5. Takeaways for Today
1. Where graphs databases fit into your existing IT portfolio
2. What are others doing with graphs, particularly in
Financial Services?
3. How can you use graphs to advance your own business

6. Latency &
Freshness
Function of your
technology Batch-
Precompute
Real-Time
Connectedness
Function of your
data & question
Illustration by David Somerville based on the original by Hugh McLeod (@gapingvoid)
Evolutions in Data Processing

7. Illustration by David Somerville based on the original by Hugh McLeod (@gapingvoid)
Evolutions in Data Processing

8. Illustration by David Somerville based on the original by Hugh McLeod (@gapingvoid)
Evolutions in Data Processing
Phase I: “Data”

9. Data Management in 1979
Paper Forms
Tiny RAM Spinning Platters
(Low Capacity / Sequential IO)

10. The RDBMS Era
(1979 - Present)

11. Key-Value, Column-Family,
Document Database
Aggregate-Oriented* NoSQL DBMSs
The NoSQL Era
(Circa 2009 - Present)
Source: Martin Fowler, NoSQL Distilled https://martinfowler.com/books/nosql.html

12. Evolutions in Data Processing
Phase II: “Information”
Illustration by David Somerville based on the original by Hugh McLeod (@gapingvoid)
RDBMS
&
Aggregate-
Oriented
NoSQL

13. Hordes of Data Hoardes of Data
Data Management Circa 2005

14. Trending & Aggregation Finding Needles in Haystacks
Data Management Circa 2005

15. Data Management Circa 2005
Commodity Server Farms Cheap & Abundant
Storage

16. The Hadoop Era
(2005 - Present)

17. Evolutions in Data
Phase III: Data Relationships
Illustration by David Somerville based on the original by Hugh McLeod (@gapingvoid)
RDBMS
&
Aggregate-
Oriented
NoSQL
Hadoop /
MapReduce

18. The internet
Data Management in 2017

19. Data Management in 2017

20. Data Management in 2017

21. Data Management in 2017
Dynamic Real-World Systems
Abundant RAM
SSD/Flash
(High-Capacity Storage &
Ultra-Fast Random I/O)

22. The Graph Era
(Now & the Future)

23. The Graph Era
(Now & the Future)

24. Illustration by David Somerville based on the original by Hugh McLeod (@gapingvoid)
RDBMS
&
Aggregate-
Oriented
NoSQL
Hadoop /
MapReduce
|<————————- Graph Database & ————————>|
Graph Compute Engine
Connected DataDiscrete Data
A View of the Data Management Portfolio

25. Latency &
Freshness
(Function of your technology)
Batch-
Precompute
Real-Time

26. Insight Action
Data Professionals
Direct Access to Data
Customer + Employeers
+ Autonomous Devices
Access via Applications
“Data Warehousing/
Analytic/OLAP/Off-Line”
“Real-Time / Transactional/
Operational/OLTP”
Another View of the Data Management Portfolio
Systems of Insight vs. Action

27. Data Professionals
Direct Access to Data
Customer + Employeers
+ Autonomous Devices
Access via Applications
“Data Warehousing/
Analytic/OLAP/Off-Line”
“Real-Time / Transactional/
Operational/OLTP”
Another View of the Data Management Portfolio
Systems of Insight vs. Action

28. Real-Time Processing
Recommendations
based on activity
from yesterday
Batch Processing
Overnight/Intermittent
Loading and Calculations
Results in lag between activity
& knowledge response
System-wide local pre-calculations
are computationally inefficient
Real-Time Writes &
Writes
Up-to-the-moment freshness
“Just-in-time” processing
most efficient for “local” queries
Recommendations
that reflects your
latest activity
Another View of the Data Management Portfolio
Systems of Insight vs. Action

29. Latency &
Freshness
Batch-
Precompute
Real-Time
Connectedness
Illustration by David Somerville based on the original by Hugh McLeod (@gapingvoid)
Neo4j Solves Connected, Real-Time Problems

31. UNIQUE ADVANTAGES OF A
NATIVE GRAPH DATABASE

32. Intuitiveness
Speed
Agility

33. 33
A unified view for
ultimate agility
• Easily understood
• Easily evolved
• Easy
collaboration
between business
and IT
#1 Benefit: Project Agility
The Whiteboard Model Is the Physical Model

34. Connectedness and Size of Data Set
ResponseTime
Relational and
Other NoSQL
Databases
0 to 2 hops
0 to 3 degrees
Thousands of connections
1000x
Advantage
Tens to hundreds of hops
Thousands of degrees
Billions of connections
Neo4j
“Minutes to
milliseconds”
#2 Benefit:
“Minutes to Milliseconds” Real-Time Query Performance

35. “We found Neo4j to be literally thousands of times faster
than our prior MySQL solution, with queries that require
10-100 times less code. Today, Neo4j provides eBay with
functionality that was previously impossible.”
- Volker Pacher, Senior Developer
“Minutes to milliseconds” performance
Queries up to 1000x faster than RDBMS or other NoSQL
#3 Benefit:
“Minutes to Milliseconds” Real-Time Query Performance

36. At Write Time:
data is connected
as it is stored
At Read Time:
Lightning-fast retrieval of data and relationships
via pointer chasing
Index free adjacency
Key Ingredient #1 of 3:
Graph Optimized Memory & Storage

37. 37
Example HR Query in SQL The Same Query using Cypher
MATCH (boss)-[:MANAGES*0..3]->(sub),
(sub)-[:MANAGES*1..3]->(report)
WHERE boss.name = “John Doe”
RETURN sub.name AS Subordinate,
count(report) AS Total
Project Impact
Less time writing queries
• More time understanding the answers
• Leaving time to ask the next question
Less time debugging queries:
• More time writing the next piece of code
• Improved quality of overall code base
Code that’s easier to read:
• Faster ramp-up for new project members
• Improved maintainability & troubleshooting
Key Ingredient #2 of 3:
A Productive and Powerful Graph Query Language

38. Graph Transactions Over
ACID Consistency
Graph Transactions Over
Non-ACID DBMSs
38
Maintains Integrity Over
Time
Becomes Corrupt Over Time
Key Ingredient #3 of 3:
ACID Graph Writes

39. VALUE FROM GRAPHS
IN FINANCIAL SERVICES

40. Asset Graph
1
Customer Graph2
Payment Graph3Master Data Graph 4
Entitlement Graph 5
THE FIVE
GRAPHS OF
FINANCE

41. #1 Asset Graph

42. Bond
z
HAS_INTEREST
Hedge
Fund
Mutual
Fund
Stock
OWNS
OWNS
Stock
ETF
OWNS
OWNS
Stock
ISSUES
HAS
Options
ON
ISSUES
COMPANY
HAS
O
W
NS
#1 Asset Graph

43. #1 Asset Graph

45. #1 Asset Graph

46. Impact analysis
Portfolio analytics
Risk assessment
Trading
Dynamic Pricing
Key Applications
Asset Graph – Key Values
Example Neo4j-customers

47. #2 Customer Graph

48. Manager
Research
VP
Dallas
Director
United States
Central
Region
CEO
North America
Strategy
ANALYST
Wholesale
Banking
Texas
#2 Customer Graph

50. Upsell/Cross-Sell
Customer Targeting
Sales Operations
Human Capital Management
Key Applications
Customer Graph – Key Values
Example Neo4j-industries
Manufacturing Health CareFinance
Telecom Retail Human Resources

51. m
#3 Payment Graph

52. #3 Payment Graph
SMB
SMB
SMB
SMB
SMB
SMB
SMB
SMB
Am
ount: $18,000
Transactions: 10
Amount: $22,000
Transactions: 200
SOLD_TO
SOLD_TO
SOLD_TO
Amount: $32,000Transactions: 170
Am
ount: $22,000
Transactions: 200
SO
LD_TO
SMB
SMB
SMB
Amount: $8,000
Transactions: 14
SOLD_TOAmount: $24,000Transactions: 11
SOLD_TO
Amount: $17,000
Transactions: 300
SOLD_TO
Am
ount: $11,000
Transactions: 199
SOLD_TO
Amount:$15,000
Transactions:10
SOLD_TOAmount:$15,000
Transactions:10
SOLD_TO

53. A Graph of Money Laundering
#3 Payment Graph

54. INVESTIGATE
Revolving Debt
Number of Accounts
INVESTIGATE
Normal behavior
Fraud Detection with Discrete Analysis
Pros
Stops rookies
Cons
False positives
False negatives

55. Revolving Debt
Number of Accounts
Normal behavior
Fraudulent pattern
Fraud Detection with Connected Analysis
Pros
Detects fraud rings
Reduces false negatives

56. Government
Example Neo4j-customers
Offers & Recommendations
Network Effects
Chargebacks
Anti Fraud / Money Laundering
Credit Risk
Key Applications
Payment Graph – Key Values

57. #4 Master Data Graph

58. Systems Planning, Impact Analysis, Data Governance, Micro-Services
Enterprise Architecture | System of Systems
#4 Master Data Graph

59. Extracts from “Graph databases for exploring metadata” by Jeremy Ponser
#4 Master Data Graph
Enterprise Metadata Graphs

60. #4 Master Data Graph
VP
Staff Staff StaffStaff
DirectorStaffDirector
Manager Manager Manager Manager
Fiber
Link
Fiber
Link
Fiber
Link
Ocean
Cable
Switch Switch
Router Router
Service
• Organizational Structures including sales
territories, reporting structures, geography
• Product Structures including product &
feature hierarchies, time dimension
• Network Inventories including configuration
management, physical and logistics networks
Enterprise Hierarchies

61. Example Neo4j-customers
360° View of the Customer
Packaging & Product Bundling
Recommendations
Human Capital Management
Key Applications
Master Data Graph – Key Values

62. #5 Entitlement Graph

63. GraphConnect 2013: https://vimeo.com/76821847
Using Graph Databases in Real-Time to
Solve Resource Authorization at Telenor

64. GraphConnect 2013: https://vimeo.com/76821847
Using Graph Databases in Real-Time to
Solve Resource Authorization at Telenor

65. GraphConnect 2013: https://vimeo.com/76821847
Using Graph Databases in Real-Time to
Solve Resource Authorization at Telenor

67. Compliance
Faster onboarding
Real-time provisioning
Real-time deprovisioning
The Value
Entitlement Graph – Key Values

68. “Graph analysis is possibly the single most
effective competitive differentiator for
organizations pursuing data-driven operations
and decisions after the design of data capture.”
“By the end of 2018, 70% of leading organizations
will have one or more pilot or proof-of-concept
efforts underway utilizing graph databases.”
Towards Graph Inevitability

69. #5 Entitlement Graph#4 Master Data Graph
#3 Payment Graph#2 Customer Graph#1 Asset Graph
The Five Graphs of Finance

70. NEW YORK CITY
April 18, 2017
09:00-09:30
09:30-10:15
10:15-11:00
11:00-11:30
11:30-12:30
12:30-13:30
13:30-17:00
Breakfast and Registration
The Connected Data Imperative: Why
Graphs
Transform Your Data: A worked example
Break
Enterprise Ready: A Look at
Neo4j in Production
Lunch
Training Session
Agenda

71. Key Analytics Patterns
HDFS/MapReduce/Spark
(Storage & Aggregation)
Streaming
(Filtering & Aggregation)
Machine LearningGraph Computation