Neo4j GraphDay Seattle- Sept19- in the enterprise

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

2. Neo4j Enterprise Edition
Native Graph Platform
Graph Overview

3. 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
3

4. Neo4j 3.0: A New Architecture Foundation
4
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

5. 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
5
Neo4j Advantage – Scalability

6. Neo4j 3.1 Highlights
Security
Foundation
Database
Kernel and
Operations
Advances
6
IBM Power8
CAPI Flash
Support
Schema
Viewer
Causal
Clustering
State-of-the-Art
Cluster
Architecture

7. Highlights of Neo4j 3.2
May 2017 GA
Enterprise scale
for global
applications
Continuous
improvement in
native performance
Enterprise governance
for the
connected enterprise
7
sa group
uk group
us_east group
hk group

8. 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

9. 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

10. Features in Community and Enterprise Editions
10
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

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

12. 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

13. 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

14. Neo4j in the Enterprise
Native Graph Differentiation
Graph Overview

15. 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

16. 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
16

17. 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

18. 18
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”

19. 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
1
9

20. 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

21. Graph Transactions Over
ACID Consistency
Graph Transactions Over
Non-ACID DBMSs
21
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

22. Neo4j Graph Platform
23
Transactions Analytics
Data Integration
API ETL SaaS
DatabaseTooling
Discover&Visualize
CUSTOMERS
BUSINESS
USERS
DEVELOPERS
ADMINS
DATA
SCIENTISTS
OTHER SYSTEMS
APPS AI / ML

23. 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
24
Neo4j
Commercial
Value

24. Case Studies
Neo4j Case Studies

25. 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 Operations26
CE Customer since 2016 Q1

26. 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 Recommendations27

27. 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 Monitoring28
CE Customer since 2016 Q1EE Customer since Q2 2017

28. 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 / Metadata29
CE Customer since 2016 Q1EE Customer since 2015

29. 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 Management30
CE users since 2017

30. 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 Management31

31. 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 / Metadata32
CE Customer since 2016 Q1EE Customer since 2015

32. 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 Things33
EE Customer since 2016 Q4

33. 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 Management34

34. 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 Management35

35. 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 Engine36
EE Customer since 2014 Q2

36. Neo4j Enterprise Edition
Native Graph Platform
Graph Overview