Dirk Möller - Neo4j

1. Gob Summit - Madrid
#1 Database for Connected Data
Dirk Möller
Director Sales CEMEA
dirk@neo4j.com
19/11/2019

2. Neo4j GraphTalks
Network & Application Management
• Einführung in Graphdatenbanken und Neo4j (9.30-10.00)
Bruno Ungermann
• Neue Herangehensweisen für Network und Application Mgt mit Graphen (10.00-11.00)
Stefan Kolmar
• Wie werden Graphdatenbank-Projekte mit Neo4j zum Erfolg? (11.00-11.30)
Stefan Kolmar
• Q&A

3. Agenda
• Impact of Graphs
• State of the Graph
• Three waves
• What‘s enabling all of this?

5. ACCOUNT
ADDRESS
PERSON
PERSON
NAME
STREET
BANK
NAME
COMPANY
BANK
BAHAMAS
2.6 TB
11.5 million documents
Emails, Scanned Documents,
Bank Statements etc…

6. 2.6 TB
11.5 million documents
Emails, Scanned Documents,
Bank Statements etc… Person
B
Bank US
Account
123
Person
A
Acme
Inc
Bank
Bahama
s
Address
XNODE
RELATIONSHIP

7. 2.6 TB
11.5 million documents
Emails, Scanned Documents,
Bank Statements etc…

11. ICIJ Pulitzer Price Winner 2017

15. State of the graph

16. “Forrester estimates that over
25% of enterprises will be using
graph databases by 2017.”
Forrester, 2014

17. Popularity of Graphs
DB-engines Ranking of Database Categories
• Graph DBMS
• Key-value stores
• Document stores
• Wide column store
• RDF stores
• Time stores
• Native XML DBMS
• Object oriented DBMS
• Multivalue DBMS
• Relational DBMS
Graph DB
2013 2014 2015 2016 2017 2018

18. Software
Financial
Services Telecom
Retail & Consumer
Goods
Media &
Entertainment Other Industries
Airbus
Over 300 Enterprises and 10s of
Thousands of Projects on Neo4j

19. 7 of the Top 10 Software
Companies Use Neo4j

20. 8 of the Top 10 Insurance
Companies Use Neo4j

21. Category Defining Use Cases
airbnb
Fraud
Detection
Real-Time
Recommendations
Network & IT
Operations
Master Data
Management
Knowledge
Graph
Identity & Access
Management

22. 10M+
Downloads
3M+ from Neo4j Distribution
7M+ from Docker
Events
400+
Approximate Number of
Neo4j Events per Year
50k+
Meetups
Number of Meetup
Members Globally
Largest pool of graph technologists
50k+
Trained/certified Neo4j
professionals
Trained Developers

23. 2012  2018
May 10th-11th, London
CONFERENCE
+
TRAINING

25. of enterprises are using
graph databases
As of today
Source: Forrester Vendor Landscape:
Graph Databases, October 6, 2017

26. "Neo4j continues to
dominate the graph
database market.”
“69% of enterprises
have, or are planning
to implement graphs
over next 12
months”
October, 2017
“The most widely stated
reason in the survey for
selecting Neo4j was
to drive innovation”
February, 2018
Critical Capabilities for
DBMSA
“In fact, the rapid rise of
Neo4j and other graph
technologies may signal
that data connectedness
is indeed a separate
paradigm from the model
consolidation happening
across the rest of the
NoSQL landscape.”
March, 2018
Graph is a Unique Paradigm

28. Three waves

29. Our core belief is — connections between data
are as important as the data itself

30. Reveal connections? Look at this data

31. Look at the same data as a graph

33. Graph Based Success

35. Retail
7 of top 10
Finance
20 of top 25 7 of top 10
Software
Hospitality
3 of top 5
Telco
4 of top 5
Airlines
3 of top 5
Logistics
3 of top 5
76%
FORTUNE 100
have adopted or
piloted Neo4j

36. Real-Time
Recommendations
Dynamic Pricing
Artificial Intelligence
& IoT-applications
Fraud Detection
Network
Management
Customer
Engagement
Supply Chain
Efficiency
Identity and Access
Management
Relationship-Driven Applications

37. 37
• Record “Cyber Monday” sales
• About 35M daily transactions
• Each transaction is 3-22 hops
• Queries executed in 4ms or less
• Replaced IBM Websphere commerce
• 300M pricing operations per day
• 10x transaction throughput on half the
hardware compared to Oracle
• Replaced Oracle database
• Large postal service with over 500k
employees
• Neo4j routes 7M+ packages daily at peak,
with peaks of 5,000+ routing operations per
second.
Handling Large Graph Work Loads for Enterprises
Real-time promotion
recommendations
Marriott’s Real-time
Pricing Engine
Handling Package
Routing in Real-Time

38. Home
Security
Internet of
things
Institutional
Memory
Entertainment
Recommendations
Home
Operations
Personalization
Voice Enabled Smart Home

39. More Data Enables
More Use Cases

40. Data Network Effect
“A product, generally powered by machine learning, becomes smarter
as it gets more data from your users. The more users use your product,
the more data they contribute; the more data they contribute, the
smarter your product becomes.”
— Matt Turck

41. What’s Enabling All of This?

42. A year ago…

43. 43
Neo4j Graph Platform
Development &
Administration
Analytics
Tooling
BUSINESS USERS
DEVELOPERS
ADMINS
Graph
Analytics
Graph
Transactions
Data Integration
Discovery & Visualization
DATA
ANALYSTS
DATA
SCIENTISTS
Drivers & APIs
APPLICATIONS
AI

44. Neo4j Bloom

46. Case Studies
Some examples from our customers

47. CONTEXTO
PATRONES
PERSPECTIVA
PRESENTACIÓN BY GRAPH EVERYWHERE

48. Graph
Database
Relational
Database
Una forma de representar información
Muy Flexible
Consultas en tiempo
real
Altamente contextual
Estructurado
Pre-calculado
Basado en reglas rígidas
PRESENTACIÓN BY GRAPH EVERYWHERE

49. Grafos: Las redes sociales
Scoring de
recomendación
de personas
PRESENTACIÓN BY GRAPH EVERYWHERE

50. RELACIONAL: AMIGOS DE MIS AMIGOS
PRESENTACIÓN BY GRAPH EVERYWHERE

51. a b
Cypher: Perfecto para patrones
c
PRESENTACIÓN BY GRAPH EVERYWHERE

52. NEO4J: AMIGOS DE MIS AMIGOS
Pedro Josep
Encontrar amigos de amigos en una red
social, hasta una profundidad máxima de
cinco niveles.
Red de 1.000.000 de personas
Cada persona tiene unos 50 amigos aprox.
Bea Gregor Cristina
PRESENTACIÓN BY GRAPH EVERYWHERE

53. NEO4J: MAS ALLA DE LA SIMPLICIDAD
Nivel
Profund
Relacional
Tiempo (seg)
Neo4j
Tiempo
(seg)
Registros
Devueltos
2 0.016 0.01 ⋍ 2.500
3 30.267 0.168 ⋍ 110.000
4 1543.505 1.359 ⋍ 600.000
5 No Termina 2.132 ⋍ 800.000
*Partner y Vukotic’s Experiment - Graph Databasesby Ian Robinson, Jim
Webber, and Emil Eifrem
PRESENTACIÓN BY GRAPH EVERYWHERE

54. Informes para los jueces:
ORGA
MEJORA
SOLUCIÓN
3
OBJETIVO
l
RETO
Q
TIEMPO IMPLEMENTACIÓN
w
PRESENTACIÓN BY GRAPH EVERYWHERE

55. PRESENTACIÓN BY GRAPH EVERYWHERE

57. Análisis crimen organizado
RETO
Q
SOLUCIÓN
3
OBJETIVO
l
TIEMPO IMPLEMENTACIÓN
w
PRESENTACIÓN BY GRAPH EVERYWHERE

58. Fraude en concesiones de
subvenciones
RETO
Q
SOLUCIÓN
3
MEJORA
l
TIEMPO
IMPLEMENTACIÓN
w
PRESENTACIÓN BY GRAPH EVERYWHERE

59. Fraude interno
RETO
Q
SOLUCIÓN
3
MEJORA
l
TIEMPO
IMPLEMENTACIÓN
w
PRESENTACIÓN BY GRAPH EVERYWHERE

60. CONTEXTO
PATRONES
PERSPECTIVA
PRESENTACIÓN BY GRAPH EVERYWHERE