Neo4j GraphTalks
Herzlich Willkommen!
June 2015
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
Beispiel: Logisches Modell Logistikprozess
Relationales Schema (“die Welt in Tabellen pressen”):
Graphenmodell, kein Schema
The Whiteboard Model Is the Physical Model
An intuitive approach to data problems
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
“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, 2014https://www.gartner.com/doc/2852717/it-market-clock-database-managementTechRadar™: Enterprise DBMS, Q1 2014http://www.forrester.com/TechRadar+Enterprise+DBMS+Q1+2014/fulltext/-/E-RES106801Graph 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/
2012 2015
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
bookon Graph
Databases
$11M Series A from Fidelity,
Sunstoneand Conor
$11M Series B from Fidelity,
Sunstoneand Conor
CommercialLeadership
First native
graph DB in 24/7
production
Invented property
graph model
Contributed first graph DB to open
source
$2.5M SeedRound from
Sunstone and Conor
Funding
Extended graph data model to
labeled property graph
150+ customers
50K+ monthlydownloads
500+ graph DB eventsworldwide
$20M Series C led by Creandum,
with Dawn and existing investors
TechnicalLeadership
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
Neo4j Adoption by Selected VerticalsFinancialServices
Communications
Health &Life
Sciences
HR &Recruiting
Media &Publishing
SocialWeb
Industry & Logistics
Entertainment Consumer Retail Information Services
Business Services
How Customers Use Neo4jNetwork &
Data Center
Master DataManagementSocial
Recom–mendation
s
Identity &
Access
Search &Discover
yGEO
Background• 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: LogisticsUse case: Real-time Recommendations for RoutingGermany
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: RetailUse case: Real-Time RecommendationsBentonville, 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
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: RetailUse case: Routing RecommendationsSan 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
Industry: CommunicationsUse case: Real-Time RecommendationsSan 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
Industry: CommunicationsUse case: Network & IT OpsParis
Background• Second largest communications company in
France• Part of Vivendi Group, partnering with Vodafone
Business problemInfrastructure 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
DEPENDS_ON
Switch Switch
Router
Fiber Link Fiber Link
Fiber Link
Oceanfloor Cable
DEPE
NDS_
ON
DEPENDS_O
N
DEPENDS_ONDEPEN
DS_ONDEPENDS_ON
DEPENDS_ON
DEPENDS_ONDEPENDS_ON
DEP
END
S_O
N
LINKE
D
LINKED
LINKED
DEPENDS_ON
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 ServicesUse case: Network & IT OperationsGlobal
Large Investment Bank
Industry: CommunicationsUse case: ID & Access ManagementOslo
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
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 ServicesUse case: ID & Access ManagementLondon
Large Investment Bank
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 ServicesUse case: Fraud DetectionLondon & New York
Large Financial Services Co.
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: TelecommunicationsUse 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
• 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: CommunicationsUse case: Graph-based Search San Jose, CA
• 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: CommunicationsUse case: Master Data Management, HMPSan Jose, CA
Neo Technology, Inc Confidential
Fragen?
Präsentationen Videos...
Sammlung Use Cases
Beispiel-Modelle