The world around us is full of connected information. Neo4j was originally developed to solve two complex "network" problems in a document management system, as it was too hard to manage rich connection information efficiently in traditional and new "NOSQL" databases.During this meetup, we will talk about the technology, and about the journey that a couple of technologists from Malmö took. You will learn* how Neo Technology grew from just the three founders in to a global database company with use-cases in every domain imaginable.* how focusing on customer and community feedback allows us to provide a solution for managing connected data to everyone, not just the large internet companies. Of course we will also introduce the graph model, it's whiteboard friendlyness and how you get started with Neo4j and it's easy and powerful query language Cypher. We'll also compare the graph and relational data model to see how they differ in shape and capabilities. Finally we discuss the foundations that enable Graph databases to provide higher join performance, faster development processes and more inclusive software for all stakeholders. With use-cases from Gaming, Dating and Finance we'll see how to apply the graph capabilities to these domains to realize new functionality or opportunities that were not possible before. Finally, if there's a question you've always wanted to ask/discuss, we'll have plenty of time for that at the end of Michael's presentation.

1. Graphs & Neo4j
Past - Present - Future
Meetup Sept 27, Stockholm - NetEnt

2. Michael Hunger
Caretaker General Neo4j Community

3. (Michael)*[:WORKS_FOR]*>(Neo4j)
michael@neo4j.com |?@mesirii?|?github.com/jexp?|?jexp.de/blog
Michael?Hunger??* Caretaker?General?@Neo4j

8. KNOWS
KNOWS
NAME: ANN
AGE: 32
SINCE: 2012
KNOWS
NODE
PROPERTY
RELATIONSHIPS

9. KNOWS
KNOWS
KNOWS
NAME: EVE
AGE: 32
SINCE: 2012
KNOWS
SELL
VIEWS
SELL
PRICE:10$
PALO ALTO
PRICE:5$
OAKLAND
SELL

12. 15 years later…

13. “Graph analysis is the true killer
app for Big Data.”
“Forrester estimates that over 25%
of enterprises will be using graph
databases by 2017.”
Today It Is An Industry

14. “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.”
Today It Is An Industry

15. Neo4j Adoption by Selected Verticals
SOFTWARE
FINANCIAL
SERVICES
RETAIL MEDIA & OTHER
SOCIAL
NETWORKS
TELECOM HEALTHCARE

19. How did we get here?

20. Let‘s build a,
1. database
2. query language
3. billion $,company
how hard can it be?

21. Our Founders
Our Culture

23. Be Open Listen!

26. Foster
Community & Collaboration
Appreciate
Empower
Support

27. Make it easy

31. Graph&Query&Language:&Cypher
(jan:Person {,name:“Jan”},) 3[:LOVES]3>,(anne:Person {,name:“Anne”},)
LOVES
Jan Anne
NODE NODE
LABEL PROPERTYLABEL PROPERTY
MATCH

32. 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
Find%all%reports%and%how%many%
people%they%manage,%
up%to%3%levels%down
Cypher'Query
SQL'Query

34. THE IMPACT OF GRAPHS
How Graphs Are Changing The World

35. #1
GRAPHS
FOR
GOOD

37. Biggest Leak in History
Panama Papers
Offshore Leaks Database
Snowden – NSA
WikiLeaks Cablegate
Pentagon Papers
0 5M 10M
Documents Leaked

41. Unraveling the Cancer Code with Graphs

44. #2
DISRUPTION
WITH
GRAPHS

45. Anybody with an idea
and a purpose,
can now use a
technology that is
powerful enough to
change an industry

46. 273

49. #3
BETTER
BUSINESS
WITH GRAPHS

50. Real-Time
Recommendations
Fraud
Detection
Network &
IT Operations
Master Data
Management
Graph-Based
Search
Identity & Access
Management
Sustainable Competitive
Advantage With Graphs

51. WHAT’S NEXT?
Building The Graph Database For The Future

52. What Users Love about Neo4j
Design
Whiteboard
friendliness
Develop Deploy
Great query
language
Native graph
performance

53. What Users Want More Of
Design
Whiteboard
friendliness
Develop Deploy
Great query
language
Native graph
performance
Productivity
Developer graph
applications faster
Scale
Design bigger and
faster graph apps
Operations
Deploy graph
applications
anywhere easily

55. Productivity

57. 2016 - : 3.x — Official Language Drivers
0.x — Embedded Java API2000 - 2010:
2.x — Cypher2014 - 2015:
2010 - 2014: 1.x — REST

58. Example - Java with Neo4j 2.x
Official Language Drivers

59. Official Language Drivers
Driver driver = GraphDatabase.driver( "bolt://localhost" );
try ( Session session = driver.session() ) {
StatementResult result = session.run("MATCH (u:User) RETURN u.name");
}
With Java

60. With JavaScript
var driver = Graph.Database.driver("bolt://localhost");
var session = driver.session();
var result = session.run("MATCH (u:User) RETURN u.name");
Official Language Drivers

61. With Python
driver = Graph.Database.driver("bolt://localhost")
session = driver.session()
result = session.run("MATCH (u:User) RETURN u.name")
Official Language Drivers

62. Official Language Drivers
using (var driver = GraphDatabase.Driver("bolt://localhost"))
using (var session = driver.Session())
{
var result = session.Run("MATCH (u:User) RETURN u.name");
}
With .NET

63. JavaScript Java .NET Python
Community
Drivers
Drivers
Bolt
PHP, Go,
Elixir, Ruby

64. Neo4j OGM
Spring Data Neo4j 4
Browser.Next
Spark Driver
JDBC Driver
Enabling:

65. User Defined
Procedures

66. Neo4j Execu+on Engine
Java Stored
Procedure
Applica+ons
Bolt

67. Java Stored Procedure
@Procedure
public Stream<StringResult> uuid() {
return Stream.of(new StringResult(UUID.randomUUID().toString()));
}

68. @Procedure
public Stream<StringResult> uuid() {
return Stream.of(new StringResult(UUID.randomUUID().toString()));
}
Cypher
Java Stored Procedure

73. RDF / Linked Data
Parallelization
Meta Data
Utilities
Data Integration
Date & Time
Graph Algorithms
Enabling:
Spatial

74. Operations

75. Official Docker Image

76. Ease of:
Testing
Provisioning
Scaling
Extending
Using
Enabling:

77. Configuration
Log Files
Startup Behavior
Directory Structure
Consistency

78. Scale

80. Scalability Performance
The Inherent Tension Between
Scalability And Performance

81. Unlimited Size Graphs

82. Huge Networks of:
People
Devices
Software
Genes & Proteins
Behaviors
Documents
Enabling:

83. Drivers
User Defined Procedures
Unlimited Size Graphs

84. Future
These are my personal predictions
- Stay Tuned -

85. Core-Edge Clustering
Enterprise Grade Security
User Defined Functions
3.1
beta

86. Large Scale Clusters
Basis for Sharding
Spark Co-Location
Compute Instances
Auto-Scaling
Core Edge

87. LDAP
Active Directory
default roles
fine grained roles
procedure security
later:
Kerberos
data level security
Security

88. data conversion
date & time
json
data structures
business rules
decomposition
User Defined Functions

89. Graph Compute, Cypher on X
Sharding
Compiled Runtime
3.2
4.0

90. Connected Age

91. Age of Graphs
and Graph
Databases

93. Thank You

94. Questions? ;-)
Ask Me Anything