2. Neo4j Inc. is the Creator of
a highly scalable, native graph database.
Neo4j gives any organization the ability to leverage
connections in data — in real-time
to create value
3. Neo4j - The Company
● Creator of Neo4j
● ~250 employees with HQ in Silicon
Valley, London, Munich, Paris,
Stockholm and Malmö (R&D)
Neo4j - The Product
● Neo4j - World’s leading graph database
● 2M+ downloads, adding 50k+ per month
● ~200 enterprise subscription customers
including over 50 of the Global 2000
8. Knows
Know
s
Know
s
Know
s
Social Graph
“People you may know”
Disruptor: Facebook
Industry: Media Ad-business
Bough
t
Bough
t
Viewe
d
Returned
Bough
t
Disruptor: Amazon
Industry: Retail
People &
Products“Other people also bought”
Whatche
d
W
atche
d
W
atche
d
Like
s
Like
d
Rate
d
People &
Content“You might also like”
Disruptor: Netflix
Industry: Broadcasting Media
Some Famous Graphs
31. Index free adjacency:
Unlike other database
models Neo4j
connects data as it
stores it
Index-free adjacency ensures
lightning-fast retrieval of data and
relationships
Neo4j Advantage - Performance
32. How Neo4j Differentiates from other
Databases
Visualization
Queries
Processing
Storage
Non-Native Graph DB
SQLCypher
(graphs)-[are]->(everywher
e)
Cypher/Gremlin
/Proprietary
Tabl
e
Tabl
e
Native Graph DB RDBMS
Tabl
e
Key-Valu
e
Column
Optimized for graph workloads
33. Looks different. Who cares?
• a sample social graph with ~1,000 persons
• average 50 friends per person
• pathExists(a,b) limited to depth 4
• caches warmed up to eliminate disk I/O
• Graph Locality
# persons query time
Relational database 1,000 2000ms
Neo4j 1,000 2ms
Neo4j 1,000,000 2ms
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
Graph
“Minutes to
milliseconds”
“Minutes to Milliseconds” Real-Time Query Performance
35. Why Graph Databases?
Performance - for specific workloads
Index-free adjacency: everything KNOWS its neighbours
Graph-locality: unnecessary scope is quickly moved out of sight
“Minutes to milliseconds performance”
38. NEO4j USE CASES
Real Time Recommendations
Master Data Management
Fraud Detection
Graph Based Search
Network & IT-Operations
Identity & Access Management
39. VIEWED
GRAPH THINKING:
Real Time Recommendations
VIEWED
BOUGHT
VIEWED
BOUGHT
BOUGHT
BOUGHT
BOUGHT
NEO4j USE CASES
Real Time Recommendations
Master Data Management
Fraud Detection
Graph Based Search
Network & IT-Operations
Identity & Access Management
40. “As the current market leader in graph
databases, and with enterprise features for
scalability and availability, Neo4j is the
right choice to meet our demands.” Marcos Wada
Software Developer,
Walmart
NEO4j USE CASES
Real Time Recommendations
Master Data Management
Fraud Detection
Graph Based Search
Network & IT-Operations
Identity & Access Management
41. GRAPH THINKING:
Master Data Management
MANAGE
S
MANAGE
S
LEADS
REGION
M
ANAG
ES
MANAGE
S
REGION
LEADS
LEADS
COLLABORATES
NEO4j USE CASES
Real Time Recommendations
Master Data Management
Fraud Detection
Graph Based Search
Network & IT-Operations
Identity & Access Management
42. Neo4j is the heart of Cisco HMP: used for
governance and single source of truth and a
one-stop shop for all of Cisco’s hierarchies.
NEO4j USE CASES
Real Time Recommendations
Master Data Management
Fraud Detection
Graph Based Search
Network & IT-Operations
Identity & Access Management
50. “Graph databases offer new methods of
uncovering fraud rings and other
sophisticated scams with a high-level of
accuracy, and are capable of stopping
advanced fraud scenarios in real-time.”
Gorka Sadowski
Cyber Security
Expert
NEO4j USE CASES
Real Time Recommendations
Master Data Management
Fraud Detection
Graph Based Search
Network & IT-Operations
Identity & Access Management
52. Uses Neo4j for network topology
analysis for big telco service
providers
NEO4j USE CASES
Real Time Recommendations
Master Data Management
Fraud Detection
Graph Based Search
Network & IT-Operations
Identity & Access Management
53. GRAPH THINKING:
Identity And Access Management
TRUSTS
TRUSTS
ID
ID
AUTHENTICATES
AUTHENTICATES
O
W
NS
OWNS
CAN_READ
NEO4j USE CASES
Real Time Recommendations
Master Data Management
Fraud Detection
Graph Based Search
Network & IT-Operations
Identity & Access Management
54. UBS was the recipient of the 2014
Graphie Award for “Best Identity
And Access Management App”
NEO4j USE CASES
Real Time Recommendations
Master Data Management
Fraud Detection
Graph Based Search
Network & IT-Operations
Identity & Access Management
56. A pattern matching query language made for graphs
56

Cypher the SQL of Graphs
● Declarative
○ You tell Cypher what you want, not how to do it
● Expressive
○ Syntax optimized for reading by humans
● Pattern Matching
○ Patterns are easy for your human brain
57. Pattern in our Graph Model
LOVES
Dan Ann
NODE NODE
Relationship
58. Pattern in our Graph Model
LOVES
Dan Ann
NODE NODE
Relationship
() --> ()
59. Cypher: Express Graph Patterns
(:Person { name:"Dan"} ) -[:LOVES]-> (:Person { name:"Ann"} )
LOVES
Dan Ann
LABEL PROPERTY
NODE NODE
LABEL PROPERTY
Relationship
TYPE
60. Cypher: CREATE Graph Patterns
CREATE (:Person { name:"Dan"} ) -[:LOVES]-> (:Person { name:"Ann"} )
LOVES
Dan Ann
LABEL PROPERTY
NODE NODE
LABEL PROPERTY
Relationship
TYPE
61. Cypher: MATCH Graph Patterns
MATCH (:Person { name:"Dan"} ) -[:LOVES]-> ( whom ) RETURN whom
LOVES
Dan ?
VARIABLE
NODE NODE
LABEL PROPERTY
Relationship
TYPE
73. • Relationships are first class citizen
• No need for joins, just follow pre-materialized relationships of nodes
• Query & Data-locality – navigate out from your starting points
• Only load what’s needed
• Aggregate and project results as you go
• Optimized disk and memory model for graphs
High Query Performance with a Native Graph DB
74. Index free adjacency:
Unlike other database
models Neo4j
connects data as it
stores it
Index-free adjacency ensures
lightning-fast retrieval of data and
relationships
Neo4j Advantage - Performance
75. name: Tom Hanks
born: 1956
title: Cloud Atlas
released: 2012
title: The Matrix
released: 1999
name: Lana Wachowski
born: 1965
ACTED_IN
roles: Zachry
ACTED_IN
roles: Bill Smoke
DIRECTED
DIRECTED
ACTED_IN
roles: Agent Smith
name: Hugo Weaving
born: 1960
Person
Movie
Movie
Person Director
ActorPerson Actor
Whiteboard friendliness
80. If it’s not stored in tables how is it stored?
• Data stored on disk is all linked lists of fixed size records. Properties are stored as a linked list of property records, each holding a
key and value and pointing to the next property. Each node and relationship references its first property record. The Nodes also
reference the first relationship in its relationship chain. Each Relationship references its start and end node. It also references the
previous and next relationship record for the start and end node respectively.
• Example of a node and its relationship(s) stored on disk:
83. The components of a Cypher query
MATCH path = (:Person)-[:ACTED_IN]->(:Movie)
RETURN path
MATCH and RETURN are Cypher keywords
path is a variable
:Movie is a node label
:ACTED_IN is a relationship type
87. Car
Property Graph Model Components
Nodes
• Represent the objects in the graph
• Can be labeled
Person Person
88. Car
DRIVES
Property Graph Model Components
Nodes
• Represent the objects in the graph
• Can be labeled
Relationships
• Relate nodes by type and direction
LOVES
LOVES
LIVES WITH
OW
NS
Person Person
89. Car
DRIVES
name: “Dan”
born: May 29, 1970
twitter: “@dan”
name: “Ann”
born: Dec 5, 1975
since:
Jan 10, 2011
brand: “Volvo”
model: “V70”
Property Graph Model Components
Nodes
• Represent the objects in the graph
• Can be labeled
Relationships
• Relate nodes by type and direction
Properties
• Name-value pairs that can go on
nodes and relationships.
LOVES
LOVES
LIVES WITH
OW
NS
Person Person
90. Summary of the graph building blocks
• Nodes - Entities and complex value types
• Relationships - Connect entities and structure domain
• Properties - Entity attributes, relationship qualities, metadata
• Labels - Group nodes by role
93. Tom Hanks Hugo Weaving
Cloud Atlas
The Matrix
Lana
Wachowski
ACTED_IN
ACTED_IN ACTED_IN
DIRECTED
DIRECTED
Whiteboard friendliness
94. name: Tom Hanks
born: 1956
title: Cloud Atlas
released: 2012
title: The Matrix
released: 1999
name: Lana Wachowski
born: 1965
ACTED_IN
roles: Zachry
ACTED_IN
roles: Bill Smoke
DIRECTED
DIRECTED
ACTED_IN
roles: Agent Smith
name: Hugo Weaving
born: 1960
Person
Movie
Movie
Person Director
ActorPerson Actor
Whiteboard friendliness
96. How do you use Neo4j
- Often asked because Neo4j is more than a database
97. Neo4j Fits into Your Enterprise Environment
Data Storage and
Business Rules Execution
Data Mining
and Aggregation
Application
Graph Database Cluster
Neo4j Neo4j Neo4j
Ad Hoc
Analysis
Bulk Analytic
Infrastructure
Graph Compute Engine
EDW …
Data
Scientist
End User
Databases
Relational
NoSQL
Hadoop
98. In your persistence layer, switch to
• Official Neo4j Drivers with Cypher
• Community Drivers
• Neo4j-JDBC Driver
• Object-graph-mapping library
• Neo4j-ogm
• Spring Data Neo4j
• Py2neo
• neo4j-php-client (PHP)
• Other libraries available for analytics pipeline,
ETL and BI tools
•
Using Neo4j from your Application
99. The world is a graph – everything is connected
• people, places, events
• companies, markets
• countries, history, politics
• sciences, art, teaching
• technology, networks, machines,
applications, users
• software, code, dependencies,
architecture, deployments
• criminals, fraudsters and their behavior
100. Why Graphs
THE MODEL
Gives you a hi-fi representation of reality
Is so much easier to store highly connected domains
Is very flexible in dynamic, agile environments