A presentation delivered at Neo4j GraphDay Chicago

1. 6/28/2017
1
Enterprise Ready: Neo4j in Production
#1 Database for Connected Data
Jeff Morris
Head of Product
Marketing
6/27/17
Enterprise Ready
Neo4j in Production
June 2017

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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
Graph is Top Trending Database Type

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Neo4j Enterprise Metadata &
Content Mangement Use Cases
Sample of Connected Graphs
Organization Identity & Access Network & IT Ops

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Background
• SF-based C2C rental platform
• Dataportal democratizes data access for
growing number of employees while improving
discoverability and trust
• Data strewn everywhere—in silos, in segmented
departments, nothing was universally accessible
Business Problem
• Data-driven culture hampered by variety and
dependability of data, tribal knowledge and
word-of-mouth distribution
• Needed visibility into information usage, context,
lineage and popularity across company of 3,000+
Solution and Benefits
• Offers search with context & metadata, user &
team-centric pages for origin & lineage
• Nodes are resources: data tables, dashboards,
reports, users, teams, business outcomes, etc.
• Relationships reflect consumption, production,
association, etc.
• Neo4j, Elasticsearch, Python
Airbnb Dataportal TRAVEL TECHNOLOGY
Knowledge Graph, Metadata Management8
CE users since 2017
Background
• Large global bank
• Deploying Reference Data to users and systems
• 12 data domains, 18 datasets, 400+ integrations
• Complex data management infrastructure
Business Problem
• Master data silos were inflexible and hard to
consume
• Needed simplification to reduce redundancy
• Reduce risk when data is in consumers’ hands
• Dramatically improve efficiency
Solution and Benefits
• Data distribution flows improved dramatically
• Knowledge Base improves consumer access
• Ad-hoc analytics improved
• Governance, lineage and trust improved
• Better service level from IT to data consumers
UBS FINANCIAL SERVICES
Master Data Management / Metadata9
CE Customer since 2016 Q1EE Customer since 2015

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Background
• 5 year long drug discovery research
• Parse & Navigate over 25 Million scientific papers
• Sourced from National Library of Research and
tagging of “Medical Subject Headers” (MeSH tags)
Business Problem
• Seeking to automate phenotype, compound and
protein cell behavior research by using previously
documented research more effectively
• Text mining for research elements like DNA strings,
proteins, RNA, chemicals and diseases
Solution and Benefits
• Found ways to identify compound interaction
behavior from millions of research documents
• Relations between biological entities can be
identified and validated by biologic experts
• Still very challenging to keep up-to-date, add
genomics data, and find a breakthrough
Novartis PHARMACEUTICAL RESEARCH
Content Management / Biomedical Research10
CE Customer since 2016 Q1CE Customer since 2012
Background
• How Neo4j is used in investigations
• Non-technical reporters manually gather data
• “Low-tech” data curation
• Journalists want to model data as a story, not
as data
Business Problem
• Identify repeated business relationships among
individuals and their holdings and accounts
• Scan documents and identify possible entities,
then create relationships between people and
documents.
• Names and alias variances
Solution and Benefits
• Uses Neo4j in “story discovery” phase
• Uncovers shortest paths for leads for reporters
• Many investigations underway now
Columbia University EDUCATION
Investigative Journalism / Fraud Detection11
CE Customer since 2016 Q1EE Customer since 2015 Q4

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Background
• eBay Israel, Entity Management Platform
• Taxonomy for hundreds of thousands of entities like
categories, products, sellers, sales, buyers, stores,
etc.
• Entities have permanent “souls” and “states” in
which they exist throughout their lifecycle
• All to make editing product items easy and fast
Business Problem
• Users demand high interactivity isolated
workspaces with inheritance for building pages
• Support versioning of entities so that users can
easily make changes, while preserving history of
its previous states
Solution and Benefits
• Chose Neo4j for performance, flexibility,
developer productivity
• Easy to learn
• Flexible way to represent how data entities
change throughout their lifecycle
• Patent pending
eBay Israel ONLINE RETAILER
Master Data Management / Metadata12
CE Customer since 2016 Q1EE Customer since 2015 Q4
Background
• French Telecom
• Big Data Governance in support for GDPR
• Environment with Hadoop, Analytics,
Recommendation engines, etc.
Business Problem
• Manage people, roles & rights, flow, audit, log
management, processes, policies, lineage,
metadata, lifecycles, security, etc…
• All because GDPR arrives in May 2018
Solution and Benefits
• Governance system oversees all systems
• Enforces correct policies
• Allows flexibility beyond Hadoop
• Architect has written Neo4j French manual
ORANGE TELECOMMUNICATIONS
Master Data Management / Metadata13
CE Customer since 2016 Q1EE Customer since 2015

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Neo4j in the Enterprise
Native Graph Differentiation
Graph Overview
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
15
Neo4j Advantage - Agility

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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
17
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”

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Open Source
(Available to anyone)
Apache 2.0
Open Source
(As part of Neo4j)
GPL v3
Open Process
(Open to anyone)
CIR, CIP, oCIM
Formal Standard
(Standards Body)
e.g. ANSI, ISO
openCypher
Documentation, TCK, Grammar, Parser
Opening the Language
Databases
Tools
ruruki
Vendor Support & Interest

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Graph Composition
Cypher
Query
Return
SQL Integration
SQL Cypher
Next Steps for Cypher
Feb ’17, May ’17, Sep '17
oCIM Summer of Syntax
Twice-Monthly Calls
Cypher Language Group
Github
Cypher Improvement
Requests & Proposals
Evolving the Language Together

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22
Graph Visualizations in Neo4j Browser

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One more thing…
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 Node keys
De-normalized or duplicated data Create separate nodes
Join tables Relationships
Join table columns Relationship properties

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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
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
2
7

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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
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

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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
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
3
1
Social Recommendation Example
Neo4j Advantage - Performance

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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
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)

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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
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
3
5

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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
Neo4j Performance Improvements by Version
0
2000
4000
6000
8000
10000
12000
14000
Neo4j 2.2 Neo4j 2.3 Neo4j 3.0 Neo4j 3.1 Neo4j 3.2
Complex Mixed-Workload Throughput
32%
50%
27%
70%
320%
Faster
than 2.2

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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
38
Neo4j Advantage – Scalability
Global Cluster
Topologies
Geo Aware
Load Balancing
Tiered Replicas
Full-Stack API
US EAST GROUP
UK GROUP
HK GROUP
SA GROUP
Now in Neo4j 3.2: Multi-DC Clustering

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How Causally Consistent Reads Work
App ServerApp Server
DriverDriver
3:
Review
Profile
4:
Create
an order
Async
Replication
Raft
Replication
1: Read
Product
Catalog
Core
Server
Core
Server
Replica
Server
App ServerApp Server
DriverDriver
App ServerApp Server
DriverDriver
ENTERPRISE
EDITION
2. Create
Account
5:
Review
orders
How it Works:
• Application chooses a consistency level
“Read Any” vs “Read your own writes”
• Cluster chooses appropriate members
Default optimizes for scalability
(i.e. read replica server for reads)
Causal Clustering Enables:
• Application-driven SLAs
• Optimizing for freshness vs. cost
• Tunability within an application
On an application & session basis
1: Read any replica | 2: Write [Tx 101] | 3: RYOW*[Tx 101] | 4: Write [Tx 102] | 5: RYOW [Tx 102]
Graph Transactions Over
ACID Consistency
Graph Transactions Over
Non-ACID DBMSs
41
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

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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)42
Case Studies for Knowledge Graphs
and Recommendation Engines
Neo4j Case Studies

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Networks are Graphs
network topology

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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

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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
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.

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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.
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.

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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.
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.

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Background
• Large Nordic Telecom Provider
• 1M Broadband routers deployed in Sweden
• Half of subscribership are over 55yrs old
• Each household connects 10 devices
• Goal to improve customer experience
Business Problem
• Broadband router enhancement to improve
customer experience
• Context-based in home services
• How to build smart home platform that allows
vendors to build new “home-centric” apps
Solution and Benefits
• New Features deployed to 1M homes
• API-based platform for easy apps that:
• Automatically assemble Spotify playlists
based on who is in the house
• Notify parents when children get home
• Build smart shopping lists
TELIA ZONE TELECOMMUNICATIONS
Smart Home / Internet of Things55
EE Customer since 2016 Q4
Background
• Large Public University – “U-Dub”
• IT staff for 80K+ students and employees
• Transforming IT systems from mainframe to cloud
• Providing IT & data warehousing services to 3
campuses, 6 hospitals, and 6,300 EDW users
Business Problem
• Old Sharepoint metadata was too complicated
for users, not flexible and not transparent
• $1B project to migrate HR system from
mainframe to Workday needed to be smooth
• Future projects needed repeatable predictability
• Needed new glossary, impact analysis, analytics
Solution and Benefits
• Consulted with NDU peers, built simple model
• Built Visualizer with Elasticsearch, Neo4j & D3.js
• Improved predictability, lineage, and impact
understanding for over 6,300 users
University of Washington EDUCATION & RESEARCH
Metadata Management, IT & Network Operations56
CE Customer since 2016 Q1

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Background
• Ad-Tech supplier in NYC identifies "intent signals"
• Collects device-born consumer data from mobile,
desktops & tablets
• Contains device and buyer data on more than
90% of American households
• Supersized Graph
Business Problem
• Recognize buyer receptivity to offers near time of
purchase
• Device data and consumer behaviors change
frequently
• Triangulate who is holding a device, where and
when it happens, to signal active purchase intent,
and create real-time offers to assist user
Solution and Benefits
• 3 Billion nodes, 9 billion relationships
• 1 Billion daily transactions on 3 servers
• Hybrid solution with Neo4j, Hadoop, Spark,
MongoDB and Ruby
• Breakthrough results from 60%-250%
higher than industry benchmarks
Qualia ADVERTISING TECHNOLOGY
Social Network, Internet of Things, and Real-Time Buyer Identification57
EE Customer since 2014 Q3
Background
• World's largest hospitality / hotel company
• 7th largest web site on internet
• 1.5 M hotel rooms offered online by 2018
• Revenue Management System that allows
property managers to update their pricing rates
Business Problem
• Provide the right room & price at the right time
• Old rate program was inflexible and bogged down
as they increased the pricing options per property
per day
• Lay the path to be an innovator in the future
Solution and Benefits
• 2016-era rate program embeds Neo4j as "cache"
• Created a graph per hotel for 4500 properties in 3
clusters
• 1000% increase in volume over 4 years
• 50% decrease in infrastructure costs
• "Use Neo4j Support!"
MARRIOTT TRAVEL & HOSPITALITY SERVICES
Pricing Recommendations Engine58
EE Customer since 2014 Q2

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Background
• Personal shopping assistant
• Converses with buyer via text, picture and voice
to provide real-time recommendations
• Combines AI and natural language understanding
(NLU) in Neo4j Knowledge Graph
• First of many apps in eBay's AI Platform
Business Problem
• Improve personal context in online shopping
• Transform buyer-provided context into ideal
purchase recommendations over social platforms
• "Feels like talking to a friend"
Solution and Benefits
• 3 developers, 8M nodes, 20M relationships
• Needed high-performance traversals to respond
to live customer requests
• Easy to train new algorithms and grow model
• Generating revenue since launch
eBay ShopBot ONLINE RETAIL
Knowledge Graph powers Real-Time Recommendations59
EE Customer since 2016 Q3
Case Study: Knowledge Graphs at eBay

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Case Study: Knowledge Graphs at eBay
Case Study: Knowledge Graphs at eBay

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Case Study: Knowledge Graphs at eBay
Bags
Case Study: Knowledge Graphs at eBay

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Men’s Backpack
Handbag
Case Study: Knowledge Graphs at eBay
https://shopbot.ebay.com/
Try it out at:
Case Study: Knowledge Graphs at eBay

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Enterprise or Community Edition
Summary
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

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Features in Community and Enterprise Editions
69
Both Editions—GRAPH Features Database Features Architecture Features
Labeled Property Graph Model ACID Transactions Language drivers for Java, Python, C# & JavaScript
Native Graph Processing & Storage High-performance Native API HTTPS plug-in
Graph Query Language “Cypher” High-performance caching REST API
Neo4j Browser w/ Syntax Highlighting Cost-based query optimizer RPM, Azure & AWS Cloud Delivery
Fast Writes via Native Label Index
Fast Reads via Composite Indexes
Enterprise Edition—GRAPH Features Database Features Architecture Features
Database storage reallocation Query monitoring with enriched metrics Enterprise Lock Manger accesses all available cores on server
Cypher query tracing
Compiled Cypher Runtime to
accelerate common queries
Causal Clustering, core and read-replica design
Node Key schema constraints User & role-based security Multi-Data Center Support for global scale
Property existence constraints LDAP & Active Directory Integration Driver-based load balancing
Kerberos Security plug-in Driver-based Causal Clustering API exposes routing logic
Bold is new in 3.2
Licensing Options
70
Edition / Program Audience License Price Point
Community Edition IT Developers GPLv3 Free
Enterprise Edition
Fair Trade
Projects
AGPL3 Free, but must publish source code
Enterprise Edition
Real-time
applications
Commercial ~$500/month/core
Early Startups
Early Stage, <20
employees
Commercial Free until traction established
Startups w/ Traction <3M ARR Commercial $1,500/month
Most deployments require only 3 server cluster for fault tolerance & HA

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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.
71
The Largest Graph Innovation Network
3,000,000+ with 50k additional per month
Neo4j Downloads
3,000,000+ with 50k additional per month
Neo4j Downloads
225+ customers
50% from Global 2000
225+ customers
50% from Global 2000
100+
Technology and Services Partners
100+
Technology and Services Partners
450+ annual events & 10k attendees
Graph and Neo4j awareness and training
450+ annual events & 10k attendees
Graph and Neo4j awareness and training
43,000+
Neo4j Meetup Members
43,000+
Neo4j Meetup Members
50,000+
Online and Classroom Education Registrants
50,000+
Online and Classroom Education Registrants

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Users Love Neo4j
Graph Visionaries
Enterprise Customers
Graph Visionaries
Enterprise Customers
74
Partners
System Integrators
Trainers
OEMs
Partners
System Integrators
Trainers
OEMs
Cloud
IaaS, PaaSm, DBaaS
Marketplace
Cloud
IaaS, PaaSm, DBaaS
Marketplace
OSS
Community
Events
Forums
Add-Ons
The Density of the Neo4j Innovation Network
Tech
Ecosystem
OEM & Tech
Partners
Tech
Ecosystem
OEM & Tech
Partners
Graph Solutions
Data Science
Architecture
Data Models
Graph Solutions
Data Science
Architecture
Data Models
Commercial
Support
Technical Support
Packaged Services
Custom Services
Commercial
Support
Technical Support
Packaged Services
Custom Services
Education
Documents
Online Training
Classroom
Custom Onsite
Education
Documents
Online Training
Classroom
Custom Onsite
Standards
Initiatives
openCypher,
LDBS
Standards
Initiatives
openCypher,
LDBS

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The Connected Enterprise Value Proposition
Fastest path to Graph Success
Graph
Expertise
Graph
Expertise
Graph
Database
Platform
Graph
Database
Platform
Innovation
Network
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
75
Neo4j
Commercial
Value
Analysts are Invited to Attend GraphConnect NYC
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38. 6/28/2017
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Case Studies for Knowledge Graphs
and Recommendation Engines
Neo4j Case Studies