Einführung in Grap

1. Neo4j GraphTalks
Herzlich Willkommen!
June 2015

2. Neo4j GraphTalks
• 09:00-09:30 Frühstück und Networking
• 09:30-10:00 Einführung in Graphen-Datenbanken und Neo4j
(Bruno Ungermann, Neo4j)
• 10:00-10.30 Digital Asset Management bei Lufthansa
(Michael Wilmes, Senior Software Engineer Lufthansa)
• 10.30-11.00 Master Data Management bei der Bayerischen Versicherung
(Thomas Wolf, CEO iS2)
• Open End

3. Beispiel: Logisches Modell Logistikprozess

4. Relationales Schema (“die Welt in Tabellen pressen”):

5. Graphenmodell, kein Schema

6. The Whiteboard Model Is the Physical Model

7. An intuitive approach to data problems

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. “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/

10. 2012  2015

11. 2000 2003 2007 2009 2011 2013 2014 20152012
Neo4j: The Graph Database Leader
GraphConnect,
first conference
for graph DBs
First
Global 2000
Customer
Introduced
first and only
declarative query
language for
property graph
Published
O’Reilly
book
on Graph
Databases
$11M Series A
from Fidelity,
Sunstone
and Conor
$11M Series B
from Fidelity,
Sunstone
and Conor
Commercial
Leadership
First
native
graph DB
in 24/7
production
Invented
property
graph
model
Contributed
first graph
DB to open
source
$2.5M Seed
Round from
Sunstone
and Conor
Funding
Extended
graph data
model to
labeled
property graph
150+ customers
50K+ monthly
downloads
500+ graph
DB events
worldwide
$20M Series C
led by
Creandum, with
Dawn and
existing investors
Technical
Leadership

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

13. 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

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

15. Backgroun
d
• One of the world’s largest logistics carriers
• Projected to outgrow capacity of old system
• New parcel routing system
• Single source of truth for entire network
• B2C & B2B parcel tracking
• Real-time routing: up to 8M parcels per day
Business problem
• 24x7 availability, year round
• Peak loads of 3000+ parcels per second
• Complex and diverse software stack
• Need predictable performance & linear scalability
• Daily changes to logistics network: route from any
point, to any point
Solution & Benefits
• Neo4j provides the ideal domain fit:
• a logistics network is a graph
• Extreme availability & performance with Neo4j clustering
• Hugely simplified queries, vs. relational for complex routing
• Flexible data model can reflect real-world data variance much
better than relational
• “Whiteboard friendly” model easy to understand
Industry: Logistics
Use case: Real-time Recommendations for Routing
Germany

16. Neo Technology, Inc Confidential
Background
Business problem
• In the drive to provide the best customer web
experience on its walmart.com site, Walmart sought to
use data products that connect masses of complex
buyer and product data to gain super-fast insight into
customer needs and product trends
• Existing relational database couldn’t handle the
complexity of the system’s queries
Solution & Benefits
• Substituted complex batch process with Neo4j for its online
real-time recommendations
• Built a simple, real-time recommendation system with low
latency queries
• Serves up better and faster recommendations, by combining
historical and session data
Industry: Retail
Use case: Real-Time
Recommendations
Bentonville, Arkansas
• Founded in 1962, Walmart has more than 11,000 brick
and mortar stores in 27 countries
• Plus more than 2 million employees and $470 billion in
annual revenues
• Needs to provide optimal online customer experience
on its walmart.com site to compete

17. Neo Technology, Inc Confidential
Background
Business problem
• Enable customer-selected delivery inside 90min
• Maintain a large network routes covering many carriers
and couriers. Calculate multiple routing operations
simultaneously, in real time, across all possible routes
• Scale to enable a variety of services, including same-
day delivery, consumer-to-consumer shipping
(www.shutl.it) and more predictable delivery times
Solution & Benefits
• Neo4j calculates all possible routes in real time for every order
• The Neo4j-based solution is thousands of times faster than the
prior RDMS based solution
• Queries require 10-100 times less code, improving time-to-
market & code quality
• Neo4j lets the team add functionality that was not previously
possible
Industry: Retail
Use case: Routing Recommendations
San Francisco & London
• eBay seeks to expand global retail presence
• Quick & predictable delivery is an important competitive
cornerstone
• To counter & upstage Amazon Prime, eBay acquired
U.K.-based Shutl to form the core of a new delivery
service, launching eBay Now (www.ebay.com/now)
prior to Christmas 2013
• Founded in 2009, Shutl was the U.K. Leader in same-
day delivery, with 70% of the market

18. Industry: Communications
Use case: Real-Time
Recommendations
San Jose CA
• Cisco.com serves customer and business customers
with Support Services
• Needed real-time recommendations, to encourage use
of online knowledge base
• Cisco had been successfully using Neo4j for its internal
master data management solution.
• Identified a strong fit for online recommendations
Solution & Benefits
• Cases, solutions, articles, etc. continuously scraped for cross-
reference links, and represented in Neo4j
• Real-time reading recommendations via Neo4j
• Neo4j Enterprise with HA cluster
• The result: customers obtain help faster, with decreased
reliance on customer support
Background
Business problem
• Call center volumes needed to be lowered by improving
the efficacy of online self service
• Leverage large amounts of knowledge stored in service
cases, solutions, articles, forums, etc.
• Problem resolution times, as well as support costs,
needed to be lowered
Support
Case
Knowledge
Base
Article
Solution
Knowledge
Base
Article
Knowledge
Base
Article
Message
Support
Case

19. Industry: Communications
Use case: Network & IT Ops
Paris
Background
• Second largest communications company in France
• Part of Vivendi Group, partnering with Vodafone
Business problem
Infrastructure maintenance took one full week to plan,
because of the need to model network impacts
• Needed rapid, automated “what if” analysis to ensure
resilience during unplanned network outages
• Identify weaknesses in the network to uncover the need
for additional redundancy
• Network information spread across > 30 systems, with
daily changes to network infrastructure
• Business needs sometimes changed very rapidly
Solution & Benefits
• Flexible network inventory management system, to support
modeling, aggregation & troubleshooting
• Single source of truth (Neo4j) representing the entire
network
• Dynamic system loads data from 30+ systems, and allows
new applications to access network data
• Modeling efforts greatly reduced because of the near 1:1
mapping between the real world and the graph
• Flexible schema highly adaptable to changing business
requirements
Router
Service
Switch Switch
Router
Fiber Link
Fiber Link
Fiber Link
Oceanfloor Cable
DEPENDS_ON
DEPENDS_ON
DEPENDS_ON
LINKED
DEPENDS_ON

20. Background
• One of the world’s oldest and largest banks
• More than 100 years old and includes more than
1000 predecessor institutions
• 500,000 employees and contractors
• Most processing is done on UNIX. Needed to
manage & visualize the approximately 50,000 UNIX
servers
Business problem
• Improve performance on company-wide network
configuration
• Combine log data from Splunk into an application that
plays events over a visualization of the network, detect
incidents
• Leverage M&A legacy systems, with no room for error
Solution & Benefits
• Use Neo4j to store UNIX server & network configuration
companywide
• Original RDBMS solution could handle only 5000
servers. Neo4j introduced for performance
• New applications also were built much more rapidly
using Neo4j than possible with SQL
Industry: Financial Services
Use case: Network & IT Operations
Global
Large
Investment
Bank

21. Industry: Communications
Use case: ID & Access Management
Oslo
Background
• 10th largest Telco provider in the world, leading in the
Nordics
• Online self-serve system where large business admins
manage employee subscriptions and plans
• Mission-critical system whose availability and
responsiveness is critical to customer satisfaction
Business problem
• Degrading relational performance. User login taking minutes
while system retrieved access rights
• Millions of plans, customers, admins, groups.
Highly interconnected data set w/massive joins
• Nightly batch workaround solved the performance problem,
but led to outdated data
• Primary system was Sybase. Batch pre-compute
workaround projected to reach 9 hours by 2014: longer than
the nightly batch window
Solution & Benefits
• Moved authorization functionality from Sybase to Neo4j
• Modeling the resource graph in Neo4j was straightforward,
as the domain is inherently a graph
• Able to retire the batch process, and move to real-time
responses: measured in milliseconds
• Users able to see fresh data, not yesterday’s snapshot
• Customer retention risks fully mitigated
• Performance, Mi->millsec, Simplicity, Understand Bus
Rules, Scale
Subscription
Account
Customer
Customer
SUBSCRIBED_BY
CONTROLLED_BY
PART_OF
User
USER_ACCESS

22. Background
• Top investment bank, headquarters Switzerland
• Using a relational database coupled with Gemfire for
managing employee permissions to research
resources (documents and application services)
Business problem
• When a new investment manager was onboarded,
permissions were manually provisioned via a complex
manual process. Traders lost an average of 7 days of
trading, waiting for the permissions to be granted
• Competitor had implemented a project to accelerate the
onboarding process. Needed to respond quickly.
• High stakes: Regulations leave no room for error.
• High complexity: Granular permissions mean each
trader needed access to hundreds of resources.
Solution & Benefits
• Organizational model, groups, and entitlements stored in
Neo4j
• Meets & exceeds performance requirements.
• Significant productivity advantage due to domain fit
• Graph visualization makes it easier for the business to
provision permissions themselves
• Moving to Neo4j meant “fewer compromises” than a
relational data store
• Now using Neo4j for authorization behind online
brokerage business
Industry: Financial Services
Use case: ID & Access Management
London
Large
Investment
Bank

23. Background
•The global cost of fraud and identity theft is estimated to be
over $200 billion per year
• Global financial services firm: trillions of dollars in total
assets
• Varying compliance & governance considerations
• Incredibly complex transaction systems, with ever-
growing opportunities for fraud
Business problem
• Needed to spot and prevent fraud detection in real time,
especially in payments that fall within “normal” behavior
metrics
• Needed more accurate and faster credit risk analysis for
payment transactions
• Needed to dramatically reduce chargebacks
Solution & Benefits
• Neo4j helped them simplify both the credit risk analysis
and fraud detection processes, lowering TCO
• Uniquely identify entities and connections
• Chargebacks and fraud greatly reduced, huge savings
• Empower business-unit teams to build Neo4j applications
for real-time use, and easily evolve them to include non-
uniform data, avoiding sparse tables and frequent schema
changes
Industry: Financial Services
Use case: Fraud Detection
London & New York
Large Financial
Services Co.

24. Background
Business problem Solution & Benefits
• Tre is part of Hutchison Whampoa, one of the world’s
largest telecommunications conglomerates
• Operates in the Nordics and U.K.
• A Neo4j cluster, containing a graph of customer billing
information, is accessed by customer-facing applications
• Neo4j’s graph-based model enables timely & insightful
profiling of customers to support customer service
• New applications & enhancements are developed faster
• Queries running much faster thanks to Neo4j
Industry: Telecommunications
Use case: Master Data Management (Customer
Data)
Stockholm, Schweden
• New business requirement to give customers more
insight into their own usage patterns
• Changing the data model was slow and painful
• New queries were difficult to write
• Very large data sets creating serious performance
problems in RDBMS for connected queries (>L2)
• Tre saw value in moving towards real-time customer
profiling and real-time analytics

25. • One of the world’s largest communications equipment
manufacturers
• #91 Global 2000. $44B in annual sales.
• Had experienced success with Neo4j in Master Data
Management and Real-time Recommendations projects,
so wanted to use it for this content management /
Graph-based Search problem
Solution & Benefits
• Cisco created a new “Intelligent Query Service,” an internal
document discovery system with automated keyword
assignment
• Sales reps report that the time it takes to find precisely the
right asset decreased from 2 weeks to 20 minutes
Background
Business problem
• Sales reps wasted days looking for appropriate materials
to send prospects
• Keyword indexing system was too slow
• Deal sales cycles were suffering
Industry: Communications
Use case: Graph-based Search
San Jose, CA

26. • One of the world’s largest communications equipment
manufacturers
• #91 Global 2000. $44B in annual sales.
• Needed a system that could accommodate its master
data hierarchies in a performant way
• HMP is a Master Data Management system at whose
heart is Neo4j. Data access services available 24x7 to
applications companywide
Solution & Benefits
• Cisco created a new system: the Hierarchy Management Platform
(HMP)
• Allows Cisco to manage master data centrally, and centralize data
access and business rules
• Neo4j provided “Minutes to Milliseconds” performance over Oracle
RAC, serving master data in real time
• The graph database model provided exactly the flexibility needed to
support Cisco’s business rules
• HMP so successful that it has expanded to
include product hierarchy
Background
Business problem
• Sales compensation system had become unable to meet
Cisco’s needs
• Existing Oracle RAC system had reached its limits:
• Insufficient flexibility for handling complex
organizational hierarchies and mappings
• “Real-time” queries were taking > 1 minute!
• Business-critical “P1” system needs to be continually
available, with zero downtime
Industry: Communications
Use case: Master Data
Management, HMP
San Jose, CA

27. Neo Technology, Inc Confidential
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bruno.ungermann@neotechnology.com