Neo4j GraphDay Seattle- Sept19- Connected data imperative

1. • Connected Data Imperative
• Graphs-Boosted Artificial Intelligence
• Break
• Enterprise Ready—Neo4j in Production
• Lunch
• Neo4j Training
• Reception
Neo4j Graph Day Seattle

2. Connected Data Imperative
#1 Database for Connected Data
Jeff Morris
Head of Product
Marketing
jeff@neo4j.com
9/19/17

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

4. 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.
The Age of Connections

5. Discrete Data Problems Connected Data Problems
Perspective
SELECT foo
FROM emp
SQL
(Ann)-[:LOVES]->(Dan)
CypherQuery
Language
RDBMS GRAPH DB
DBMS
Architectur
e

6. Graph is Top Trending Database Type

7. Neoj4’s Amazing Customers
NASA explores graph database
for deep insights into space
International Consortium of Investigative Journalists
Wins Pulitzer Prize

8. Business Problem
• Find relationships between people, accounts,
shell companies and offshore accounts
• Journalists are non-technical
• Biggest “Snowden-Style” document leak ever;
11.5 million documents, 2.6TB of data
Solution and Benefits
• Pulitzer Prize winning investigation resulted in
robust coverage of fraud and corruption
• PM of Iceland resigned, exposed Putin, Prime
Ministers, gangsters, celebrities (Messi)
• Trials ongoing
Background
• International Consortium of Investigative
Journalists (ICIJ), small team of data journalists
• International investigative team specializing in
cross-border crime, corruption and accountability
of power
• Works regularly with leaks and large datasets
ICIJ Panama Papers INVESTIGATIVE JOURNALISM
Fraud Detection / Graph-Based Search8

9. “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.
“Forrester estimates that over 25% of enterprises will be using
graph databases by 2017”
IT Market Clock for Database Management Systems, 2014
https://www.gartner.com/doc/2852717/it-market-clock-database-management
TechRadar™: Enterprise DBMS, Q1 2014
http://www.forrester.com/TechRadar+Enterprise+DBMS+Q1+2014/fulltext/-/E-RES106801
Making Big Data Normal with Graph Analysis for the Masses, 2015
http://www.gartner.com/document/3100219
Analyst Expectations Three Years Ago
9

10. The Largest Graph Innovation Network
3,000,000+ with 50k additional per month
Neo4j Downloads
250+ customers, 500+ startups
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

11. Graph Visionaries
Enterprise Customers
11
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

12. October 23 & 24 New York City
The premier conference for graph technology
Speakers Include:

13. SOFTWARE
FINANCIAL
SERVICES
RETAIL MEDIA
SOCIAL
NETWORKS
TELECOM HEALTH
Neo4j Adoption

14. Users Love Neo4j

15. 15
“Neo4j continues to dominate the
graph database market.
It’s not surprising but Neo4j rules!”
Noel Yuhanna
Forrester Market Overview:
Graph Database Vendors
September 2017

16. 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

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

18. Neo4j Graph Platform
18
Transactions Analytics
Data Integration
API ETL SaaS
DatabaseTooling
Discover&Visualize
CUSTOMERS
BUSINESS
USERS
DEVELOPERS
ADMINS
DATA
SCIENTISTS
OTHER SYSTEMS
APPS AI / ML

19. Solving Massive-Scale Challenges:
Recommendations
19
People, Places, Things +
Interests +
Transactions +
Activity
Each requires a new & higher
level of scaling

20. Pros
Simple
Stops rookies
Traditional Fraud Detection: Discrete Data Analysis
Revolving
Debt
INVESTIGATE
INVESTIGATE
Number of accounts
Cons
False positives
False negatives
20

21. “Connected Analysis” : next generation Fraud detection
Revolving
Debt
Number of accounts
ADVANTAGES
Detect fraud rings
Fewer false negatives
21
Stop fraud attempts in flight:
Carry out graph “walks” in real-time for transactions &
key events, revealing suspicious “loops”
Speed manual fraud analysis:
Empower fraud analysts with graph-based analysis, to
more quickly and accurately identify complex fraud

22. Gartner’s Layered Fraud Prevention Approach (4)
(4) http://www.gartner.com/newsroom/id/1695014
Next Gen Fraud Prevention: Entity Linking
Analysis
of users
and their
endpoints
Analysis of
navigation
behavior and
suspect patterns
Analysis of
anomaly
behavior by
channel
Analysis of
anomaly
behavior
correlated
across channels
Analysis of
relationships
to detect
organized crime
and collusion
Layer 1
Endpoint-
Centric
Navigation-
Centric
Account-
Centric
Cross-
Channel
Entity
Linking
Layer 2 Layer 3 Layer 4 Layer 5
DISCRETE DATA ANALYSIS CONNECTED ANALYSIS
22

23. Why is Neo4j Succeeding? KISS
Focus on Simplifying the Adoption, Awareness and Success with Graphs
Open Source business model
• Commitment to developers – DevRel, Training, Events, etc.
• Commitment to sharing Cypher, the open graph query language on Apache
Native Graph Technology Leadership
• Commitment to data integrity, scale and performance
• Expanding User Communities to Data Scientists, Analysts & Business Users
Highest Investment in Customer Success
• Applications offer real impact, and we spread these stories

24. The Connected Enterprise Value Proposition
Fastest path to Graph Success
Graph
Expertise
Graph
Platform
Analytics +
OLTP
Innovation
Network
Enterprise-Grade
Innovation Launchpad
• Neo4j Enterprise Edition
• HA, Causal Cluster, MDC
• Better performance
• Hardened product
• Graph Analytics &
Algorithims
• Apache integrations
The Next Innovation
• Density of the network accelerates
innovation opportunity
• Thousands of project successes
• Partners, Service Providers,
Vendors, Academics, Researchers
• Driving Technology Adoption
Millions of Graph Hours
• Shrink learning curve
• Design advice
• Contextual experience
• Deploy & Ops support
24
Neo4j
Commercial
Value

25. Neo4j Features & Advantages
Neo4j Graph Platform

26. 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 Labels to classify nodes
• Labels have native indexes
Relationships
• Relate nodes by type and direction
Properties
• Attributes of Nodes & Relationships
• Stored as Name/Value pairs
• Can have indexes and composite
indexes
MARRIED TO
LIVES WITH
PERSON PERSON
26
Neo4j Advantage - Agility

27. Graph Visualizations in Neo4j Browser

28. 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

29. 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

30. Databases
Tools
ruruki
Vendor Support & Interest

31. 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

32. 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
3
3

33. 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

34. 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

35. 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

36. 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
7
Social Recommendation Example
Neo4j Advantage - Performance

37. 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

38. in the Enterprise
#1 Database for Connected Data
Jeff Morris
Head of Product
Marketing
jeff@neo4j.com
9/19/17

39. Neo4j Enterprise Edition
Native Graph Platform
Graph Overview

40. Neo4j 2.3 Release Summary
GA October 2015
Intelligent Applications
at Scale
• Higher concurrent
performance at scale with
fully off-heap cache
• Improved Cypher
performance with smarter
query planner
Developer Enablement:
Productivity & Governance
• Schema enhancements:
Property existence constraints
• String-enhanced graph search
• Spring Data Neo4j 4.0
• Numerous productivity
improvements
DevOps Enablement for On-
Premise & Cloud
• Official Docker support
• PowerShell support
• Mac installer and launcher
• Easy 3rd party monitoring
with
Neo4j Metrics
• New & improved tooling
41

41. Neo4j 3.0: A New Architecture Foundation
42
Cypher Engine
Parser
Rule-based
optimizer
Cost-based optimizer
Runtime
Neo4j
Neo4j
Application
Neo4j
New language driversNew binary protocol
Improved cost-based
query optimizer
New file, config and log structure
for tomorrow’s deployments
Native Language Drivers
BOLT
New storage engine with no limits
Enterprise Edition
Java Stored Procedures

42. 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
43
Neo4j Advantage – Scalability

43. Neo4j 3.1 Highlights
Security
Foundation
Database
Kernel and
Operations
Advances
44
IBM Power8
CAPI Flash
Support
Schema
Viewer
Causal
Clustering
State-of-the-Art
Cluster
Architecture

44. Highlights of Neo4j 3.2
May 2017 GA
Enterprise scale
for global
applications
Continuous
improvement in
native performance
Enterprise governance
for the
connected enterprise
45
sa group
uk group
us_east group
hk group

45. 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
Estimated
Neo4j 3.3

46. Global Iterative Graph Algorithms
PageRank Community Detection
2016 Presidential Debate #3
Twitter Graph
2016 Presidential Debate #3
Twitter Graph - Minus Bots
Further reading: https://medium.com/@swainjo/election-2016-debate-three-on-twitter-4fc5723a3872

47. Features in Community and Enterprise Editions
48
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

48. Neo4j Supported Platforms
On-Premise Platforms Cloud Platforms and Containers
IBM POWER
For Development
… and others

49. Why Neo4j: Key Technology Benefits
ACID Transactions
• ACID transactions with causal consistency
• Security Foundation delivers enterprise-
class security and control
Hardware Efficiency
• Native graph query processing and storage
requires 10x less hardware
• Index-free adjacency requires 10x less CPU
Agility
• Native property graph model
• Modify schema as business changes
without disrupting existing data
Developer Productivity
• Easy to learn, declarative graph query language
• Procedural language extensions
• Open library of procedures and functions
• Worldwide developer network
… all backed by Neo’s track record of leadership
and product roadmap
Performance
• Index-free adjacency delivers millions of
hops per second
• In-memory pointer chasing for fast query
results

50. 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
Classic Case Studies

51. Neo4j in the Enterprise
Native Graph Differentiation
Graph Overview

52. Discrete Data
Minimally
connected data
Neo4j is designed for data relationships
Neo4j's Connections-First Positioning & Focus
Other NoSQL Relational DBMS Neo4j Graph DB
Connected Data
Focused on
Data Relationships
Development Benefits
Easy model maintenance
Easy query
Deployment Benefits
Ultra high performance
Minimal resource usage

53. Theme: Why Non-Native Graphs Fail
Why Neo4j leads the graph market
Graph is an independent paradigm
• Driving simplicity, adoption and business value solutions
• Multi-model vendors increase complexity
• Graph value is in the hops (more than 3)
Simplify
• Express from idea to whiteboard
• Language to translate to computer
• Visualization and user experience
• ACID Transactions in a native architecture
• Scalable database stack that meets market expectations
54

54. 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

55. 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”

56. 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
5
7

57. 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

58. Graph Transactions Over
ACID Consistency
Graph Transactions Over
Non-ACID DBMSs
59
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

59. Neo4j Graph Platform
61
Transactions Analytics
Data Integration
API ETL SaaS
DatabaseTooling
Discover&Visualize
CUSTOMERS
BUSINESS
USERS
DEVELOPERS
ADMINS
DATA
SCIENTISTS
OTHER SYSTEMS
APPS AI / ML

60. The Connected Enterprise Value Proposition
Fastest path to Graph Success
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
62
Neo4j
Commercial
Value

61. Case Studies
Neo4j Case Studies

62. 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 Operations64
CE Customer since 2016 Q1

63. Business Problem
• Optimize walmart.com user experience
• Connect complex buyer and product data to gain
super-fast insight into customer needs and
product trends
• RDBMS couldn’t handle complex queries
Solution and Benefits
• Replaced complex batch process real-time online
recommendations
• Built simple, real-time recommendation system
with low-latency queries
• Serve better and faster recommendations by
combining historical and session data
Background
• Founded in 1962 and based in Arkansas
• 11,000+ stores in 27 countries with walmart.com
online store
• 2M+ employees and $470 billion in annual
revenues
Walmart RETAIL
Real-Time Recommendations65

64. Background
• Brazil's largest bank, #38 on Forbes G2000
• $61B annual sales 95K employees
• Most valuable brand in Brazil
• 28.9M credit card & 25.6M debit card accounts
• High integrity, customer-centric values
Business Problem
• Data silos made assessing credit worthiness hard
• High sensitivity to fraud activity
• 73% of all transactions over internet and mobile
• Needed real-time detection for 2,000 analysts
• Scale to trillions of relationships
Solution and Benefits
• Credit monitoring and fraud detection application
• 4.2M nodes & 4B relationships for 100 analysts
• Grow to 93T relationships for 2000 analysts by 2021
• Real time visibility into money flow across multiple
customers
Itau Unibanco FINANCIAL SERVICES
Fraud Detection / Credit Monitoring66
CE Customer since 2016 Q1EE Customer since Q2 2017

65. 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 / Metadata67
CE Customer since 2016 Q1EE Customer since 2015

66. 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 Management68
CE users since 2017

67. Background
• San Jose-based communications equipment
giant ranks #91 in the Global 2000 with $44B in
annual sales
• Needed high-performance system that could
provide master-data access services 24x7 to
applications company-wide
Solution and Benefits
• New Hierarchy Management Platform (HMP)
manages master data, rules and access
• Cut access times from minutes to milliseconds
• Graphs provided flexibility for business rules
• Expanded master-data services to include product
hierarchies
Business Problem
• Sales compensation system didn’t meet needs
• Oracle RAC system had reached its limits
• Inflexible handling of complex organizational
hierarchies and mappings
• ”Real-time” queries ran for more than a minute
• P1 system must have zero downtime
Cisco COMMUNICATIONS
Master Data Management69

68. 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 / Metadata70
CE Customer since 2016 Q1EE Customer since 2015

69. 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 Things71
EE Customer since 2016 Q4

70. Business Problem
• Needed new asset management backbone to
handle scheduling, ads, sales and pushing linear
streams to satellites
• Novell LDAP content hierarchy not flexible
enough to store graph-based business content
Solution and Benefits
• Neo4j selected for performance and domain fit
• Flexible, native storage of content hierarchy
• Graph includes metadata used by all systems:
TV series-->Episodes-->Blocks with Tags-->
Linked Content, tagged with legal rights, actors,
dubbing et al
Background
• Nashville-based developer of lifestyle-
oriented content for TV, digital, mobile and
publishing
• Web properties generate tens of millions of
unique visitors per month
Scripps Networks MEDIA AND ENTERTAINMENT
Master Data Management72

71. Business Problem
• Needed to reimagine existing system to beat
competition and provide 360-degree view of
customers
• Channel complexity necessitated move to graph
database
• Needed an enterprise-ready solution
Solution and Benefits
• Leapfrogged competition and increased digital
business by 23%
• Handles new data from mobile, social networks,
experience and governance sources
• After launch of new Neo4j MDM, Pitney Bowes
stock declared a Buy
Background
• Connecticut-based leader in digital marketing
communications
• Helps clients provide omni-channel experience
with in-context information
Pitney Bowes MARKETING COMMUNICATIONS
Master Data Management73

72. 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 Engine74
EE Customer since 2014 Q2

73. Neo4j Enterprise Edition
Native Graph Platform
Graph Overview