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Emil Eifrem Keynote - GraphConnect Europe 2017
Emil Eifrem
CEO & Co-Founder Neo4j
Welcome!
#neo4j#graphconnect
Emil Eifrem Keynote - GraphConnect Europe 2017
ACCOUNT
ADDRESS
PERSON
PERSON
NAME
STREET
BANK
NAME
COMPANY
BANK
BAHAMAS
Person
B
Bank US
Account
Person
A
Compan
y
Bank
Bahama
s
AddressNODE
RELATIONSHIP
Emil Eifrem Keynote - GraphConnect Europe 2017
Emil Eifrem Keynote - GraphConnect Europe 2017
Emil Eifrem Keynote - GraphConnect Europe 2017
Emil Eifrem Keynote - GraphConnect Europe 2017
ICIJ Pulitzer Price Winner 2017
Neo4j Connected Data Fellowship Award
www.neo4j.com/datafellowship
Finding a Cure for Cancer
Emil Eifrem Keynote - GraphConnect Europe 2017
Emil Eifrem Keynote - GraphConnect Europe 2017
Emil Eifrem Keynote - GraphConnect Europe 2017
PHARMA DB GENOMIC DATA PATIENT
RECORDS
TRIAL DATA DRUGS DB LAB DATA
Data Stored in Disparate Silos
Johnson Space Center
Houston, Texas
Emil Eifrem Keynote - GraphConnect Europe 2017
Emil Eifrem Keynote - GraphConnect Europe 2017
Emil Eifrem Keynote - GraphConnect Europe 2017
“Lessons Learned Database”
A half-century of collective NASA engineering knowledge
“Lessons Learned”
Database
GRAPH DB
Load Data
“Lessons Learned”
Database
GRAPH DB
Load Data
Let’s Hear a Few Stories
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
Emil Eifrem Keynote - GraphConnect Europe 2017
Neo4j Adoption
“Forrester estimates that over
25% of enterprises will be using
graph databases by 2017.”
Forrester Research, 2014
Adoption Highlights
Retail
7 out of top 10
retailers in the world
Finance
12 out of 25 top
financial services firms
8 out of top 10
software vendors
Software
(As per 2017)
Real-Time
Recommendation
s
Fraud
Detection
Network &
IT
Operations
Master Data
Management
Graph-Based
Search
Identity & Access
Management
Common Graph DB Use Cases
Startup Adoption
≤ 20 employees
Free Neo4j Enterprise Subscription
(Unsupported)
≤ 50 employees
Startup Pricing
(Includes Support)
Tiers, Neo4j Startup Program
Emil Eifrem Keynote - GraphConnect Europe 2017
Emil Eifrem Keynote - GraphConnect Europe 2017
Sustainable Competitive Advantage
Case Study: The Consumer Web
Connections in Data
Ability to Leverage Connections
Connections in Data Ability to Leverage Connections
Function of Time Choice of Technology
Good for discrete problems
Insufficient for connected
problems
RDBMS
Data lake
Good for Analytics, BI, Map Reduce
Non-Operational, Slow Queries
RDBMS
of the Global 2000 are
Using or Piloting Neo4j!
As of today
Philip Rathle
VP of Product, Neo4j
openCypher
Graph Query Language
Discrete Data
Problems
Connected Data
Problems
Perspective
SELECT foo
FROM emp
SQL
(Ann)-[:LOVES]->(Dan)
CypherQuery
Language
RDBMS GRAPH DB
DBMS
Architecture
Cypher
Users Cypher
Most Used
Sep 2015May 2015Jan 2015Sep 2014May 2014Jan 2014Sep 2013May 2013
100
Popularitychanges
500
600
700
200
300
400
Jan 2013
© DB-Engines.com 2015
• Wide column stores
• RDF stores
• Document stores
• Search engines
• Native XML DBMS
• Key-value stores
• Object oriented DBMS
• Multivalue DBMS
• Times Series DBMS
Relational database
Graph Database
Launch of Cypher
Open Source
(Available to anyone)
Apache 2.0
Opening the Language
Open Source
(As part of Neo4j)
GPL v3
Open Process
(Open to anyone)
CIR, CIP, oCIM
Formal Standard
(Standards Body)
e.g. ANSI, ISO
Documentation, TCK, Grammar, Parser
openCypher Resources
Databases
Tools
ruruki
Cypher is Evolving
Graph Composition
Cypher
Query
Return
SQL Integration
SQL Cypher
Next Steps for Cypher
Evolving the Language Together
Feb ’17, May ’17, TBD
oCIM Summer of Syntax
Twice-Monthly Calls
Cypher Language Group
Github
Cypher Improvement
Requests & Proposals
What’s New in Product?
Neo4j 3.1
The Graph Foundation for the
Connected Enterprise
December 2016
Neo4j 3.2
The Graph Foundation for Global
Internet Scale Applications
I
Global Scale
Introducing Neo4j 3.2
Built for global internet
applications
Native Perfomance
III
Continuous improvements
in graph performance
II
Governance & Security
Enterprise governance for
the connected enterprise
Global Scale
Built for global internet
applications
Neo4j Enterprise 3.1
Causal Clustering
Ultra-High
Throughput
Guaranteed
Redundancy
Read-your-own-
Writes
Built-in Routing
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
I
Global Scale
Introducing Neo4j 3.2
Built for global internet
applications
Native Perfomance
III
Continuous improvements
in graph performance
II
Governance & Security
Enterprise governance for
the connected enterprise
Governance & Security
Enterprise governance for
the connected enterprise
Governance & Security Foundation
LDAP & Active
Directory
User & Roles Procedure Access
Controls
Kerberos
Strong Security
Governance & Security Foundation
Coming Soon!
Intra-Cluster Encryption
Available in Neo4j 3.2
*businessUnit
*customerID
infoProperties
Node Keys
Schema Constraints
I
Global Scale
Introducing Neo4j 3.2
Built for global internet
applications
Native Perfomance
III
Continuous improvements
in graph performance
II
Governance & Security
Enterprise governance for
the connected enterprise
Native Label Index: Writes now 30-300% faster
Composite Indexes : Exact lookups for multiple properties
Reachability Queries: Can improve by orders of magnitude
Compiled Cypher Runtime: Speeds queries up to 300%
Neo4j Browser: Ground-up rewrite yields snappier performance
Native Graph Performance
Improvements in Neo4j 3.2
One more thing…
Neo4j 3.2
Available Now
Visit neo4j.com/download
Emil Eifrem
CEO & Co-Founder Neo4j
Machine Learning
Deep Learning
Machine Learning is Based on Graphs
Machine Learning is Based on Graphs
Knowledge Graphs
Context drives meaning and
precision for Machine
Learning systems
FATHER_OF DRIVE
Knowledge Graphs
reminders in Inbox smart messaging
in Allo
Google Photos
Knowledge Graphs with Neo4j
Case Study: Knowledge Graphs at eBay
Case Study: Knowledge Graphs at eBay
Case Study: Knowledge Graphs at eBay
Case Study: Knowledge Graphs at eBay
Case Study: Knowledge Graphs at eBay
Bags
Men’s Backpack
Handbag
Case Study: Knowledge Graphs at eBay
Case Study: Knowledge Graphs at eBay
https://shopbot.ebay.com/
Try it out at:
A Highly Connected Future
Emil Eifrem Keynote - GraphConnect Europe 2017
Your Homework - Connect
Emil Eifrem Keynote - GraphConnect Europe 2017

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