Neo4j GraphDay Seattle- Sept19- Connected data imperative
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Transcript of Neo4j GraphDay Seattle- Sept19- Connected data imperative
• Connected Data Imperative
• Graphs-Boosted Artificial Intelligence
• Break
• Enterprise Ready—Neo4j in Production
• Lunch
• Neo4j Training
• Reception
Neo4j Graph Day Seattle
Connected Data Imperative
#1 Database for Connected Data
Jeff Morris
Head of Product
Marketing
9/19/17
Who We Are: The Graph Platform for Connected Data
Neo4j is an enterprise-grade native graph platform that enables you to:
• Store, reveal and query data relationships
• Traverse and analyze any levels of depth in real-time
• Add context and connect new data on the fly
• Performance• ACID Transactions• Agility• Graph Algorithms
3
Designed, built and tested natively for graphs from the start for:
• Developer Productivity• Hardware Efficiency• Global Scale• Graph Adoption
CONSUMER
DATA
PRODUCT
DATA
PAYMENT
DATA
SOCIAL
DATA
SUPPLIER
DATA
The next wave of competitive advantage will be
all about using connections to produce
actionable insights.
The Age of Connections
Discrete Data Problems Connected Data Problems
Perspective
SELECT fooFROM emp
SQL(Ann)-[:LOVES]->(Dan)
CypherQuery
Language
RDBMS GRAPH DB
DBMSArchitectur
e
Graph is Top Trending Database Type
Neoj4’s Amazing Customers
NASA explores graph database for deep insights into space
International Consortium of Investigative Journalists Wins Pulitzer Prize
Business Problem
• Find relationships between people, accounts, shell companies and offshore accounts
• Journalists are non-technical
• Biggest “Snowden-Style” document leak ever; 11.5 million documents, 2.6TB of data
Solution and Benefits
• Pulitzer Prize winning investigation resulted in robust coverage of fraud and corruption
• PM of Iceland resigned, exposed Putin, Prime Ministers, gangsters, celebrities (Messi)
• Trials ongoing
Background
• International Consortium of Investigative Journalists (ICIJ), small team of data journalists
• International investigative team specializing in cross-border crime, corruption and accountability of power
• Works regularly with leaks and large datasets
ICIJ Panama Papers INVESTIGATIVE JOURNALISM
Fraud Detection / Graph-Based Search8
“Graph analysis is possibly the single most effective competitive differentiator for organizations pursuing data-driven operations and decisions after the design of data capture.”
By the end of 2018, 70% of leading organizations will have one or more pilot or proof-of-concept efforts underway utilizing graph databases.
“Forrester estimates that over 25% of enterprises will be using graph databases by 2017”
IT Market Clock for Database Management Systems, 2014https://www.gartner.com/doc/2852717/it-market-clock-database-management
TechRadar™: Enterprise DBMS, Q1 2014http://www.forrester.com/TechRadar+Enterprise+DBMS+Q1+2014/fulltext/-/E-RES106801
Making Big Data Normal with Graph Analysis for the Masses, 2015
http://www.gartner.com/document/3100219
Analyst Expectations Three Years Ago
9
The Largest Graph Innovation Network
3,000,000+ with 50k additional per monthNeo4j Downloads
250+ customers, 500+ startups50% from Global 2000
100+ Technology and Services Partners
450+ annual events & 10k attendees Graph and Neo4j awareness and training
43,000+ Neo4j Meetup Members
50,000+ Online and Classroom Education Registrants
Graph VisionariesEnterprise Customers
11
PartnersSystem Integrators
TrainersOEMs
CloudIaaS, PaaSm, DBaaS
Marketplace
OSSCommunity
EventsForums
Add-Ons
The Density of the Neo4j Innovation Network
TechEcosystem
OEM & Tech Partners
Graph SolutionsData ScienceArchitectureData Models
CommercialSupport
Technical SupportPackaged ServicesCustom Services
EducationDocuments
Online TrainingClassroom
Custom Onsite
Standards Initiatives
openCypher, LDBS
October 23 & 24 New York CityThe premier conference for graph technology
Speakers Include:
SOFTWAREFINANCIAL
SERVICESRETAIL MEDIA
SOCIAL
NETWORKSTELECOM HEALTH
Neo4j Adoption
Users Love Neo4j
15
“Neo4j continues to dominate the graph database market.
It’s not surprising but Neo4j rules!”
Noel YuhannaForrester Market Overview:
Graph Database VendorsSeptember 2017
Real-Time Recommendations
Dynamic PricingArtificial Intelligence
& IoT-applications
Fraud DetectionNetwork
ManagementCustomer
Engagement
Supply Chain Efficiency
Identity and Access Management
Relationship-Driven Applications
Sample of Connected Graphs
Organization Identity & Access Network & IT Ops
Neo4j Graph Platform
18
Transactions Analytics
Data IntegrationAPI ETL SaaS
Da
tab
ase
To
olin
g
Dis
cove
r &
Vis
ua
lize
CUSTOMERS
BUSINESSUSERS
DEVELOPERS
ADMINS
DATASCIENTISTS
OTHER SYSTEMS
APPS AI / ML
Solving Massive-Scale Challenges: Recommendations
19
People, Places, Things +Interests +
Transactions + Activity
Each requires a new & higherlevel of scaling
ProsSimpleStops rookies
Traditional Fraud Detection: Discrete Data Analysis
RevolvingDebt
INVESTIGATE
INVESTIGATE
Number of accounts
ConsFalse positivesFalse negatives
20
“Connected Analysis” : next generation Fraud detection
RevolvingDebt
Number of accounts
ADVANTAGESDetect fraud rings
Fewer false negatives
21
Stop fraud attempts in flight: Carry out graph “walks” in real-time for transactions &
key events, revealing suspicious “loops”
Speed manual fraud analysis: Empower fraud analysts with graph-based analysis, to
more quickly and accurately identify complex fraud
Gartner’s Layered Fraud Prevention Approach (4)
(4) http://www.gartner.com/newsroom/id/1695014
Next Gen Fraud Prevention: Entity Linking
Analysis of users
and their endpoints
Analysis ofnavigation
behavior and suspect patterns
Analysis of anomaly
behavior by channel
Analysis of anomaly behavior
correlated across channels
Analysis of relationships
to detect organized crime
and collusion
Layer 1
Endpoint-Centric
Navigation-Centric
Account-Centric
Cross-Channel
Entity Linking
Layer 2 Layer 3 Layer 4 Layer 5
DISCRETE DATA ANALYSIS CONNECTED ANALYSIS
22
Why is Neo4j Succeeding? KISS
Focus on Simplifying the Adoption, Awareness and Success with Graphs
Open Source business model• Commitment to developers – DevRel, Training, Events, etc. • Commitment to sharing Cypher, the open graph query language on Apache
Native Graph Technology Leadership• Commitment to data integrity, scale and performance• Expanding User Communities to Data Scientists, Analysts & Business Users
Highest Investment in Customer Success• Applications offer real impact, and we spread these stories
The Connected Enterprise Value Proposition Fastest path to Graph Success
Graph Expertise
Graph Platform
Analytics + OLTP
Innovation Network
Enterprise-Grade Innovation Launchpad• Neo4j Enterprise Edition• HA, Causal Cluster, MDC• Better performance• Hardened product• Graph Analytics &
Algorithims• Apache integrations
The Next Innovation• Density of the network accelerates
innovation opportunity• Thousands of project successes• Partners, Service Providers,
Vendors, Academics, Researchers• Driving Technology Adoption
Millions of Graph Hours • Shrink learning curve• Design advice• Contextual experience• Deploy & Ops support
24
Neo4jCommercial
Value
Neo4j Features & Advantages
Neo4j Graph Platform
CAR
name: “Dan”born: May 29, 1970
twitter: “@dan”name: “Ann”
born: Dec 5, 1975
since: Jan 10, 2011
brand: “Volvo”model: “V70”
Neo4j Invented the Labeled Property Graph Model
Nodes• Can have Labels to classify nodes• Labels have native indexes
Relationships• Relate nodes by type and direction
Properties• Attributes of Nodes & Relationships• Stored as Name/Value pairs• Can have indexes and composite
indexes
MARRIED TO
LIVES WITHPERSON PERSON
26 Neo4j Advantage - Agility
Graph Visualizations in Neo4j Browser
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
Open Source
(Available to anyone)Apache 2.0
Open Source
(As part of Neo4j)GPL v3
Open Process
(Open to anyone)CIR, CIP, oCIM
Formal Standard
(Standards Body)e.g. ANSI, ISO
openCypher
Documentation, TCK, Grammar, Parser
Opening the Language
Databases
Tools
ruruki
Vendor Support & Interest
Relational DBMSs 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 inappropriatewhen data relationships are valuable in real-time
Slow developmentPoor performance
Low scalabilityHard to maintain
Queries can take non-sequential,arbitrary paths through data
Real-time queries need speed and consistent response times
Queries must run reliably with consistent results
Q
A single query can touch a lot of data
Relationship Queries Strain Traditional Databases
33
At Write Time:
data is connected
as it is stored
At Read Time:Lightning-fast retrieval of data and relationships via pointer chasing
Index free adjacency
Graph Optimized Memory & Storage
Neo4j: Native Graph from the Start
Native graph storageOptimized for real-time reads and ACID writes
• Relationships stored as physical objects, eliminating need for joins and join tables
• Nodes connected at write time, enabling scale-independent response times
Native graph queryingMemory structures and algorithms optimized for graphs
• Index-free adjacency enables 1M+ hops per second via in-memory pointer chasing
• Off-heap page cache improves operational robustness and scaling compared with JVM-based caches
• “Minutes to milliseconds” performance improvement
Neo4j Advantage - Performance Neo4j Advantage - ACID Transactions
Connectedness and Size of Data Set
Re
sp
on
se T
ime
Relational and Other NoSQL Databases
0 to 2 hops0 to 3 degreesThousands of connections
1000x
Advantage
Tens to hundreds of hops Thousands of degreesBillions of connections
Graph
“Minutes to milliseconds”
“Minutes to Milliseconds” Real-Time Query Performance
Equivalent Cypher Query
MATCH (you)-[:BOUGHT]->(something)<-[:BOUGHT]-(other)-[:BOUGHT]->(reco)WHERE id(you)={id}RETURN reco
Traversal Speeds on Amazon Retail Dataset
Threads Hops per second
1 3-4 million
10 17-29 million
20 34-50 million
30 36-60 million
37
Social Recommendation Example
Neo4j Advantage - Performance
Neo4j Scalability
Dynamic pointer compressionUnlimited-sized graphs with no performance compromise
Index partitioningAuto-partitioning of indexes into 2GB partitions
Causal clustering architectureEnables unlimited read scaling with ACID writes and a choice of consistency levels
Multi-Data Center SupportCreates HA, Fault Tolerant Global Applications
Efficient processingNative graph processing and storage often requires 10x less hardware
Efficient storageOne-tenth the disk and memory requirements of certain alternatives
Neo4j Advantage – Scalability
in the Enterprise
#1 Database for Connected Data
Jeff Morris
Head of Product
Marketing
9/19/17
Neo4j Enterprise EditionNative Graph Platform
Graph Overview
Neo4j 2.3 Release SummaryGA 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
41
Neo4j 3.0: A New Architecture Foundation
42
Cypher Engine
Parser
Rule-based optimizer
Cost-based optimizer
Runtime
Neo4jNeo4j
Application
Neo4j
New language driversNew binary protocol
Improved cost-based query optimizer
New file, config and log structure for tomorrow’s deployments
Native Language DriversBOLTNew storage engine with no limits
Enterprise Edition
Java Stored Procedures
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 ArchitectureModern and Fault-Tolerant to Guarantee Graph Safety
43 Neo4j Advantage – Scalability
Neo4j 3.1 Highlights
SecurityFoundation
Database Kernel and Operations Advances
44
IBM Power8 CAPI Flash
Support
SchemaViewer
CausalClustering
State-of-the-ArtCluster
Architecture
Highlights of Neo4j 3.2May 2017 GA
Enterprise scalefor global
applications
Continuous improvement in
native performance
Enterprise governancefor the
connected enterprise
45
sa group
uk group
us_east group
hk group
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
Esti
mat
ed
Neo4j 3.3
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
Features in Community and Enterprise Editions
48
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 tracingCompiled 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
Neo4j Supported Platforms
On-Premise Platforms Cloud Platforms and Containers
IBM POWER
For Development
… and others
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
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
Neo4j in the EnterpriseNative Graph Differentiation
Graph Overview
Discrete DataMinimally
connected data
Neo4j is designed for data relationships
Neo4j's Connections-First Positioning & Focus
Other NoSQL Relational DBMS Neo4j Graph DB
Connected DataFocused on
Data Relationships
Development BenefitsEasy model maintenance
Easy query
Deployment BenefitsUltra high performanceMinimal resource usage
Theme: Why Non-Native Graphs FailWhy 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
54
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
56
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 LanguageThe query asks: “Find all direct reports and how many people they manage, up to three levels down”
UNIFIED, IN-MEMORY MAP
Lightning-fast queries due toreplicated 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 DataQ R
Q R
Relationship Queries on non-native Graph Architectures
57
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
Graph Transactions OverACID Consistency
Graph Transactions OverNon-ACID DBMSs
59
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
Neo4j Graph Platform
61
Transactions Analytics
Data IntegrationAPI ETL SaaS
Da
tab
ase
To
olin
g
Dis
cove
r &
Vis
ua
lize
CUSTOMERS
BUSINESSUSERS
DEVELOPERS
ADMINS
DATASCIENTISTS
OTHER SYSTEMS
APPS AI / ML
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
62
Neo4jCommercial
Value
Case Studies
Neo4j Case Studies
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 complicatedfor 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 Operations64
CE Customer since 2016 Q1
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.comonline store
• 2M+ employees and $470 billion in annual revenues
Walmart RETAIL
Real-Time Recommendations65
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 Monitoring 66
CE Customer since 2016 Q1EE Customer since Q2 2017
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 / Metadata67
CE Customer since 2016 Q1EE Customer since 2015
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 Management68
CE users since 2017
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 Management69
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 / Metadata70
CE Customer since 2016 Q1EE Customer since 2015
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 Things71
EE Customer since 2016 Q4
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 Management72
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 marketingcommunications
• Helps clients provide omni-channel experience with in-context information
Pitney Bowes MARKETING COMMUNICATIONS
Master Data Management73
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 Engine74
EE Customer since 2014 Q2
Neo4j Enterprise EditionNative Graph Platform
Graph Overview