1.
RDBMS to Graphs
Harnessing the Power of the Graph
March 2015

2.
Agenda
• Origins of Neo4j
• Benefits of Graphs
• Your enterprise architecture & polyglot persistence
• Query time!
• Q&A

3.
Neo Technology Overview
Product
• Neo4j - World’s leading graph
database
• 1M+ downloads, adding 50k+
per month
• 150+ enterprise subscription
customers including over
50 of the Global 2000
Company
• Neo Technology, Creator of Neo4j
• 80 employees with HQ in Silicon
Valley, London, Munich, Paris and
Malmö
• $45M in funding from Fidelity,
Sunstone, Conor, Creandum,
Dawn Capital

4.
Neo4j Adoption by Selected Verticals
Financial
Services
Communications
Health &
Life
Sciences
HR &
Recruiting
Media &
Publishing
Social
Web
Industry
& Logistics
Entertainment Consumer Retail Information ServicesBusiness Services

5.
How Customers Use Neo4j
Network &
Data Center
Master Data
Management
Social Recom–
mendations
Identity
& Access
Search &
Discovery
GEO

6.
“Forrester estimates that over 25% of enterprises will be using
graph databases by 2017”
Neo4j Leads the Graph Database Revolution
“Neo4j is the current market leader in graph databases.”
“Graph analysis is possibly the single most effective competitive
differentiator for organizations pursuing data-driven operations
and decisions after the design of data capture.”
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
Graph Databases – and Their Potential to Transform How We Capture Interdependencies (Enterprise Management Associates)
http://blogs.enterprisemanagement.com/dennisdrogseth/2013/11/06/graph-databasesand-potential-transform-capture-interdependencies/

7.
Largest Ecosystem of Graph Enthusiasts
• 1,000,000+ downloads
• 20,000+ education registrants
• 18,000+ Meetup members
• 100+ technology and service partners
• 150+ enterprise subscription customers
including 50+ Global 2000 companies

8.
High Business Value in Data Relationships
Data is increasing in volume…
• New digital processes
• More online transactions
• New social networks
• More devices
Using Data Relationships unlocks value
• Real-time recommendations
• Fraud detection
• Master data management
• Network and IT operations
• Identity and access management
• Graph-based search… and is getting more connected
Customers, products, processes,
devices interact and relate to
each other
Early adopters became industry leaders

9.
Relational DBs 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

10.
Unlocking Value from Your Data Relationships
• Model your data as a graph of data
and relationships
• Use relationship information in real-
time to transform your business
• Add new relationships on the fly to
adapt to your changing business

11.
Modeling as a Graph

12.
The Whiteboard Model Is the Physical Model

13.
CAR
name: “Dan”
born: May 29, 1970
twitter: “@dan”
name: “Ann”
born: Dec 5, 1975
since:
Jan 10, 2011
brand: “Volvo”
model: “V70”
Property Graph Model Components
Nodes
• The objects in the graph
• Can have name-value properties
• Can be labeled
Relationships
• Relate nodes by type and direction
• Can have name-value properties
LOVES
LOVES
LIVES WITH
PERSON PERSON

14.
Relational Versus Graph Models
Relational Model Graph Model
KNOWS
ANDREAS
TOBIAS
MICA
DELIA
Person FriendPerson-Friend
ANDREAS
DELIA
TOBIAS
MICA

15.
Let’s Model!
Customer, Supplier, and Product (Master Data)
Orders (Activity)

16.
The Domain Model

17.
Except…

18.
The Requisite
Northwind Example!
NOT JUST ANY

19.
(Northwind)-[:TO]->(Graph)
Building the Graph Model

20.
Building Relationships in Graphs
SOLD
Employee OrderOrder

21.
Locate Foreign Keys

22.
(FKs)-[:BECOME]->(Relationships)
Correct Directions

23.
Drop Foreign Keys

24.
Find the Join Tables

25.
Simple Join Tables Becomes Relationships

26.
Attributed Join Tables Become
Relationships with Properties

27.
Working Subset (Today’s Exercise)

28.
Northwind Graph Model

29.
Querying Your Data

30.
Basic Query: Who do people report to?
MATCH (:Employee{ firstName:“Steven”} ) -[:REPORTS_TO]-> (:Employee{ firstName:“Andrew”} )
REPORTS_TO
Steven Andrew
LABEL PROPERTY
NODE NODE
LABEL PROPERTY

31.
Basic Query: Who do people report to?
MATCH
(e:Employee)<-[:REPORTS_TO]-(sub:Employee)
RETURN
*

32.
Basic Query: Who do people report to?

33.
Basic Query: Who do people report to?

34.
MATCH (sub)-[:REPORTS_TO*0..3]->(boss),
(report)-[:REPORTS_TO*1..3]->(sub)
WHERE boss.name = “John Doe”
RETURN sub.name AS Subordinate,
count(report) AS Total
Express Complex Queries Easily with Cypher
Find all direct reports and how
many people they manage,
up to 3 levels down
Cypher Query
SQL Query

35.
“We found Neo4j to be literally thousands of times faster
than our prior MySQL solution, with queries that require
10 to 100 times less code. Today, Neo4j provides eBay
with functionality that was previously impossible.”
Volker Pacher
Senior Developer

36.
Who is in Robert’s (direct, upwards) reporting chain?
MATCH
p=(e:Employee)<-[:REPORTS_TO*]-(sub:Employee)
WHERE
sub.firstName = ‘Robert’
RETURN
p

37.
Who is in Robert’s (direct, upwards) reporting chain?

38.
Who’s the Big Boss?
MATCH
p=(e:Employee)
WHERE
NOT (e)<-[:REPORTS_TO]->()
RETURN
e.firstName as bigBoss

39.
Who’s the Big Boss?

40.
Product Cross-Sell
MATCH
(choc:Product {productName: 'Chocolade'})
<-[:PRODUCT]-(:Order)<-[:SOLD]-(employee),
(employee)-[:SOLD]->(o2)-[:PRODUCT]->(other:Product)
RETURN
employee.firstName, other.productName, count(distinct o2) as count
ORDER BY
count DESC
LIMIT 5;

41.
Product Cross-Sell

42.
High Performance

43.
Neo4j Clustering
Architecture Optimized for Speed & Availability at Scale
43
Performance Benefits
• No network hops within queries
• Real-time operations with fast and
consistent response times
• Cache sharding spreads cache across
cluster for very large graphs
Clustering Features
• Master-slave replication with
master re-election and failover
• Each instance has its own local cache
• Horizontal scaling & disaster recovery
Load Balancer
Neo4jNeo4jNeo4j

44.
Getting Data into Neo4j
Cypher-Based “LOAD CSV” Capability
• Transactional (ACID) writes
• Initial and incremental loads of up to
10 million nodes and relationships
Command-Line Bulk Loader neo4j-import
• For initial database population
• For loads with 10B+ records
• Up to 1M records per second
4.58 million things
and their relationships…
Loads in 100 seconds!

45.
MIGRATE
ALL DATA
MIGRATE
GRAPH DATA
DUPLICATE
GRAPH DATA
Non-graph data Graph data
Graph dataAll data
All data
Relational
Database
Graph
Database
Application
Application
Application
Three Ways to Load Data into Neo4j

46.
Polyglot Persistence

47.
Data Storage and
Business Rules Execution
Data Mining
and Aggregation
Neo4j Fits into Your Enterprise Environment
Application
Graph Database Cluster
Neo4j Neo4j Neo4j
Ad Hoc
Analysis
Bulk Analytic
Infrastructure
Graph Compute Engine
EDW …
Data
Scientist
End User
Databases
Relational
NoSQL
Hadoop

48.
Polyglot Persistence

49.
Users Love Neo4j

50.
Users Love Neo4j

51.
Learn the Way of the Graph
Quickly and Easily

52.
Quick Start: Plan Your Project
1
2
3
4
5
6
7
8
Learn Neo4j
Decide on Architecture
Import and Model Data
Build Application
Test Application
Deploy your app
in as little as 8 weeks
PROFESSIONAL SERVICES PLAN

53.
There Are Lots of Ways to Easily Learn Neo4j

54.
Summary
Only Neo4j Unlocks the Value in Your Data Relationships
Data is increasing in volume…
• New digital processes
• More online transactions
• New social networks
• More devices
… and is getting more connected
Customers, products, processes,
devices interact and relate to
each other

55.
RDBMS to Graphs
Harnessing the Power of the Graph
End of Presentation