View the slides from 'The Connected Data Imperative' by Jeff Morris, Director of Product Marketing at Neo4j at GraphTalk Denver.

1. The Connected Data Imperative
#1 Database for Connected Data
Jeff Morris
Head of Product
Marketing
4/4/17

2. The Connected Data Imperative:
The Shifting Enterprise Data Story
May 2017

3. Who We Are: The Graph Database for Connected Data
Neo4j is an enterprise-grade native graph database that enables you to:
• Store and query data relationships
• Traverse any levels of depth on real-time
• Add and connect new data on the fly
• Performance
• ACID Transactions
• Agility
3
Designed, built and tested natively
for graphs from the start to ensure:
• Developer Productivity
• Hardware Efficiency

5. ACCOUNT
ADDRESS
PERSON
PERSON
NAME
STREET
BANK
NAME
COMPANY
BANK
BAHAMAS

6. Bank
US
Account
Person
A
Company
Bank
Bahamas
AddressNODE
RELATIONSHIP
Person
B

9. ICIJ Pulitzer Price Winner 2017

10. Finding a Cure for Cancer

14. PHARMA DB GENOMIC DATA PATIENT
RECORDS
TRIAL DATA DRUGS DB LAB DATA
Data Stored in Disparate Silos

15. Johnson Space Center
Houston, Texas

19. “Lessons Learned Database”
A half-century of collective NASA engineering
knowledge

20. Let’s Hear a Few Stories

21. Let’s Hear a Few Stories
— David Meza, Chief Knowledge Architect at
NASA
“Neo4j saved well over two years
of work and one million dollars of
taxpayer funds.”
Impact

22. Our core belief is

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

24. Relational
Database
Turn of the Century Thinking

25. 21st Century Thinking
Data Modelled as a Graph
Graph
Database

26. “Lessons
Learned”
Database
GRAPH DB
Load Data

27. “Lessons
Learned”
Database
GRAPH DB
Load Data

28. CONSUMER
DATA
PRODUCT
DATA
PAYMENT
DATA
SOCIAL
DATA
SUPPLIER
DATA
“The next wave of competitive advantage will be
all about using connections to produce
actionable insights.”

29. Graphs power the transformative companies by
highlighting the RELATIONSHIPS in Data

30. Graph is Top Trending Database Type

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

32. Introducing Neo4j Graphs
Community Overview

33. Sample of Connected Graphs
Organization Identity & Access Network & IT Ops

34. Networks are Graphs

35. network topology

37. Mesh
Router
Gateway
Mesh
Router
Router
Mesh
Router
Gateway
Router
Router
Router
Router
Access
Point
CPU
CPU CPU
CPU
Mobile
Mobile Mobile
Mobile
Base
Station
CPU
CPU
CPU
CPU
Access
Point

38. Sys Admins
Servers, on-premise virtual machines,
cloud virtual machines, etc.
Network Admins
Switches, Routers, Egress Points
App Admins
I.e. Salesforce, Marketo, SAP, Oracle
Apps, Tableau, SharePoint, DBA’s etc.
Internal Users
HR, Sales, Marketing, Data Analysts,
E-staff etc.
Numerous Customers & Partners

39. Router
Servers
Servers
Apps
FirewallCloud
Switch
Apps
Network Admins
Switches, Routers, Egress Points
Sys Admins
Servers, on-premise virtual machines,
cloud virtual machines, etc.
App Admins
I.e. Salesforce, Marketo, SAP, Oracle
Apps, Tableau, SharePoint, DBA’s etc.
Internal Users
HR, Sales, Marketing, Data Analysts,
E-staff etc.

40. Router
Servers
Servers
Apps
FirewallCloud
Switch
Apps
Network Admins
Switches, Routers, Egress Points
Sys Admins
Servers, on-premise virtual machines,
cloud virtual machines, etc.
App Admins
I.e. Salesforce, Marketo, SAP, Oracle
Apps, Tableau, SharePoint, DBA’s etc.
Internal Users
HR, Sales, Marketing, Data Analysts,
E-staff etc.

41. Router
Servers
Servers
Apps
FirewallCloud
Switch
Apps
Network Admins
Switches, Routers, Egress Points
Sys Admins
Servers, on-premise virtual machines,
cloud virtual machines, etc.
App Admins
I.e. Salesforce, Marketo, SAP, Oracle
Apps, Tableau, SharePoint, DBA’s etc.
Internal Users
HR, Sales, Marketing, Data Analysts,
E-staff etc.

42. Router
Servers
Servers
Apps
FirewallCloud
Switch
Apps
Network Admins
Switches, Routers, Egress Points
Sys Admins
Servers, on-premise virtual machines,
cloud virtual machines, etc.
App Admins
I.e. Salesforce, Marketo, SAP, Oracle
Apps, Tableau, SharePoint, DBA’s etc.
Internal Users
HR, Sales, Marketing, Data Analysts,
E-staff etc.

43. Router
Servers
Servers
Apps
FirewallCloud
Switch
Apps
Network Admins
Switches, Routers, Egress Points
Sys Admins
Servers, on-premise virtual machines,
cloud virtual machines, etc.
App Admins
I.e. Salesforce, Marketo, SAP, Oracle
Apps, Tableau, SharePoint, DBA’s etc.
Internal Users
HR, Sales, Marketing, Data Analysts,
E-staff etc.

44. >50%
of the Global 2000 are
Using or Piloting Neo4j!
As of today

45. Introducing Neo4j
Native Graph Differentiation
Graph Overview

46. CAR
name: “Dan”
born: May 29, 1970
twitter: “@dan”
name: “Ann”
born: Dec 5, 1975
since:
Jan 10, 2011
brand: “Volvo”
model: “V70”
Neo4j Invented the Labeled Property Graph Model
Nodes
• Can have name-value properties
• Can have Labels to classify nodes
Relationships
• Relate nodes by type and direction
• Can have name-value properties
MARRIED TO
LIVES WITH
PERSON PERSON
46
Neo4j Advantage - Agility

47. The Largest Graph Innovation Network
3,000,000+ with 50k additional per month
Neo4j Downloads
225+ customers
50% from Global 2000
100+
Technology and Services Partners
450+ annual events & 10k attendees
Graph and Neo4j awareness and training
43,000+
Neo4j Meetup Members
50,000+
Online and Classroom Education Registrants

48. Users Love Neo4j

49. RDBMS Vocabulary Mapped to Graph Modeling
Relational DB Construct Graph DB Construct
Entity table Node labels
Row Node
Columns Node properties
Technical primary keys Replace with business primary keys
Constraints Unique constraints for business keys
Indexes Indexes on any property
Foreign keys Relationships
Default values Not required
De-normalized or duplicated data Create separate nodes
Join tables Relationships
Join table columns Relationship properties

50. Good for discrete problems
Insufficient for connected
problems
RDBMS

51. Relational DBMSs Can’t Handle Relationships Well
• Cannot model or store data and relationships
without complexity
• Performance degrades with number and levels
of relationships, and database size
• Query complexity grows with need for JOINs
• Adding new types of data and relationships
requires schema redesign, increasing time to
market
… making traditional databases inappropriate
when data relationships are valuable in real-time
Slow development
Poor performance
Low scalability
Hard to maintain

52. Queries can take non-sequential,
arbitrary paths through data
Real-time queries need speed and
consistent response times
Queries must run reliably
with consistent results
Q
A single query can
touch a lot of data
Relationship Queries Strain Traditional Databases
5
2

53. 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
Graph Optimized Memory & Storage

54. Neo4j: Native Graph from the Start
Native graph storage
Optimized for real-time reads and ACID writes
• Relationships stored as physical objects,
eliminating need for joins and join tables
• Nodes connected at write time, enabling
scale-independent response times
Native graph querying
Memory structures and algorithms optimized for graphs
• Index-free adjacency enables 1M+ hops per second via in-
memory pointer chasing
• Off-heap page cache improves operational robustness
and scaling compared with JVM-based caches
• “Minutes to milliseconds” performance improvement
Neo4j Advantage - Performance Neo4j Advantage - ACID Transactions

55. Cypher: Powerful and Expressive Query Language
MATCH (:Person { name:“Dan”} ) -[:MARRIED_TO]-> (spouse)
MARRIED_TO
Dan Ann
NODE RELATIONSHIP TYPE
LABEL PROPERTY VARIABLE
Neo4j Advantage – Developer productivity

56. 56
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
Productivity Gains with Graph Query Language
The query asks: “Find all direct reports and how many people they manage, up to three levels down”

57. 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

58. Equivalent Cypher Query
MATCH (you)-[:BOUGHT]->(something)<-[:BOUGHT]-(other)-[:BOUGHT]->(reco)
WHERE id(you)={id}
RETURN reco
Traversal Speeds on Amazon Retail Dataset
Threads Hops per second
1 3-4 million
10 17-29 million
20 34-50 million
30 36-60 million
5
8
Social Recommendation Example
Neo4j Advantage - Performance

59. Graph databases are designed for data relationships
Discrete Data
Minimally
connected data
Fit for Purpose: The Right Architecture for the Right Job
Other NoSQL Relational DBMS Graph DB
Connected Data
Focused on
Data Relationships
Development Benefits
Easy model maintenance
Easy query
Deployment Benefits
Ultra high performance
Minimal resource usage

60. Graph
Graph DatabaseRDBMS
TabularAggregate Oriented (3)
Key-Value, Column-Family,
Document Database
Source: Martin Fowler NoSQL Distilled
Database Management Systems
Five Key Sub-Patterns (incl. SQL)

61. NoSQL Databases Don’t Handle Relationships
• No data structures to model or store
relationships
• No query constructs to support data
relationships
• Relating data requires “JOIN logic”
in the application
• No ACID support for transactions
… making NoSQL databases inappropriate when
data relationships are valuable in real-time

62. Data lake
Good for Analytics, BI, Map Reduce
Non-Operational, Slow Queries
RDBMS

63. UNIFIED, IN-MEMORY MAP
Lightning-fast
queries due to
replicated in-memory
architecture and
index-free adjacency
MACHINE 1 MACHINE 2 MACHINE 3
Slow queries
due to
index lookups +
network hops
Using Graph
Using Other NoSQL to Join Data
Q R
Q R
Relationship Queries on non-native Graph Architectures
6
3

64. Neo4j Scalability
Dynamic pointer compression
Unlimited-sized graphs with no
performance compromise
Index partitioning
Auto-partitioning of indexes into
2GB partitions
Causal clustering architecture
Enables unlimited read scaling
with ACID writes and a choice
of consistency levels
Multi-Data Center Support
Creates HA, Fault Tolerant Global
Applications
Efficient processing
Native graph processing and storage
often requires 10x less hardware
Efficient storage
One-tenth the disk and memory
requirements of certain alternatives
Neo4j Advantage – Scalability

65. Raft-based architecture
• Continuously available
• Consensus commits
• Third-generation cluster architecture
Cluster-aware stack
• Seamless integration among drivers,
Bolt protocol and cluster
• No need for external load balancer
• Stateful, cluster-aware sessions with
encrypted connections
Streamlined development
• Relieves developers from complex infrastructure concerns
• Faster and easier to develop distributed graph applications
Neo4j Enterprise: Causal Clustering Architecture
Modern and Fault-Tolerant to Guarantee Graph Safety
65
Neo4j Advantage – Scalability

66. Graph Transactions Over
ACID Consistency
Graph Transactions Over
Non-ACID DBMSs
66
Maintains Integrity Over Time Eventual Consistency Becomes Corrupt Over Time
The Importance of ACID Graph Writes
• Ghost vertices
• Stale indexes
• Half-edges
• Uni-directed ghost edges

67. Summary of Neo4j: Built for the Enterprise
Native Graph Storage
Designed, built, and tested for graphs
Native Graph Query Processing
For real-time, relationship-based apps
Evaluate millions of relationships in a blink
Whiteboard-Friendly Data Modeling
Faster projects compared to RDBMS
Data Integrity and Security
Fully ACID transactions, causal consistency
and enterprise security
Powerful, Expressive Query Language
Improved productivity, with 10x to 100x
less code than SQL
Scalability and High Availability
Architecture provides ideal balance of
performance, availability, scale for graphs
Built-in ETL
Seamless import from other databases
Integration
Fits easily into your IT environment, with
drivers and APIs for popular languages
MATCH
(A)67

68. Enterprise-Class Technology
Ready for real-time enterprise applications
Performance and Scalability
• Clustered replication across
data centers
• Unlimited graph sizes
• Intelligent online space reuse
• Enterprise lock manager
• Compiled runtime for common
queries
• Kerberos authentication add-on
• Clustering on CAPI flash add-on
Monitoring and Administration
• Advanced monitoring by role
• Cypher query tracing
• Hot backups
• Enterprise security
Enterprise Schema Governance
• Property existence constraints
• Composite and node key constraints
68

69. Enterprise-Class Expertise
Neo4j Customer Success
Expert design, development and
deployment services
• Graph and application design
• Application deployment
• Data center configuration
• Developer and user training
• World-class support with SLAs
• Support portal and knowledge base
Graph Innovation Network
Worldwide community of Neo4j and
graph database experts
• Service providers
• OEMs and VARs
• Technology partners
• Open source community
Use Neo4j experts and join the Innovation Network.
Develop your apps right the first time.
69

70. Graph Visionaries
Enterprise Customers
70
Partners
System Integrators
Trainers
OEMs
Cloud
IaaS, PaaSm, DBaaS
Marketplace
OSS
Community
Events
Forums
Add-Ons
The Density of the Neo4j Innovation Network
Tech
Ecosystem
OEM & Tech
Partners
Graph Solutions
Data Science
Architecture
Data Models
Commercial
Support
Technical Support
Packaged Services
Custom Services
Education
Documents
Online Training
Classroom
Custom Onsite
Standards
Initiatives
openCypher,
LDBS

71. The Neo4j Innovation Network is Your Fastest Path to
Your Next Great Idea
Graph
Expertise
Graph
Database
Platform
Innovation
Network
Enterprise-Grade
Innovation Launchpad
• Neo4j Enterprise Edition
• HA, Causal Cluster, MDC
• Better performance
• Hardened product
The Next Innovation
• Density of the network accelerates
innovation opportunity
• Thousands of project successes
• Partners, Service Providers,
Vendors, Academics, Researchers
Millions of Graph Hours
• Shrink learning curve
• Design advice
• Contextual experience
• Deploy & Ops support
71
Neo4j
Commercial
Value

72. Case Studies for Knowledge Graphs
and Recommendation Engines
Neo4j Case Studies

73. 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

74. Shopping Recommendations
Examples of companies that use Neo4j, the world’s leading graph database, for
recommendation and personalization engines.
Adidas uses Neo4j to combine
content and product data into a
single, searchable graph database
which is used to create a
personalized customer experience
“We have many different silos, many
different data domains, and in order to
make sense out of our data, we needed
to bring those together and make them
useful for us,”
– Sokratis Kartelias, Adidas
eBay ShopBot Personal Shopping
Companion in FB Messenger
“ShopBot uses its Knowledge Graph to
understand user requests and generate
follow-up questions to refine requests
before searching for the items in eBay’s
inventory. In a search query for “bags”
for example, purple nodes represent
“categories,” green “attributes” and
pink are “values” for those attributes.”
– RJ Pittman Blog, eBay
Walmart uses Neo4j to give
customer best web experience
through relevant and personal
recommendations
“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 Vada, Walmart
Product recommendations Personalization
Linkedin Chitu seeks to engage
Chinese jobseekers through a
game-like user interface that is
available on both desktop and
mobile devices.
“The challenge was speed,” said
Dong Bin, Manager of Development
at Chitu. “Due to the rate of growth
we saw from our competitors in the
Chinese market, we knew that we
had to launch Chitu as quickly as
possible.”
Social Network
Additional Case Studies

75. Case Study: Knowledge Graphs at eBay

76. Case Study: Knowledge Graphs at eBay

77. Case Study: Knowledge Graphs at eBay

78. Case Study: Knowledge Graphs at eBay

79. Bags
Case Study: Knowledge Graphs at eBay

80. Men’s Backpack
Handbag
Case Study: Knowledge Graphs at eBay

81. https://shopbot.ebay.com/
Try it out at:
Case Study: Knowledge Graphs at eBay

82. Real-time Package Routing
• Large postal service with over
500k employees
• Neo4j routes 7M+ packages daily
at peak, with peaks of 5,000+
routing operations per second.
Real-time promotion recommendations
• Record “Cyber Monday” sales
• About 35M daily transactions
• Each transaction is 3-22 hops
• Queries executed in 4ms or less
• Replaced IBM Websphere commerce
Real-time pricing engine
• 300M pricing operations per day
• 10x transaction throughput on half
the hardware compared to Oracle
• Presentation at
http://graphconnect.com/gc2016-sf/
• Replaced Oracle database
Recommendations, Pricing and Routing