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Neo4j GraphDay Seattle- Sept19- Connected data imperative

The document outlines an agenda for a Neo4j Graph Day event including sessions on connected data, graphs and artificial intelligence, a lunch break, Neo4j training, and a reception. Key topics include Neo4j in production environments, its role in boosting artificial intelligence, and training opportunities.

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• 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 jeff@neo4j.com 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 foo FROM emp SQL (Ann)-[:LOVES]->(Dan) CypherQuery Language RDBMS GRAPH DB DBMS Architectur 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, 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 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 month Neo4j Downloads 250+ customers, 500+ startups 50% 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 Visionaries Enterprise Customers 11 Partners System Integrators Trainers OEMs Cloud IaaS, PaaSm, DBaaS Marketplace OSS Community Events Forums Add-Ons The Density of the Neo4j Innovation Network Tech Ecosystem OEM & Tech Partners Graph Solutions Data Science Architecture Data Models Commercial Support Technical Support Packaged Services Custom Services Education Documents Online Training Classroom Custom Onsite Standards Initiatives openCypher, LDBS
October 23 & 24 New York City The premier conference for graph technology Speakers Include:
SOFTWARE FINANCIAL SERVICES RETAIL MEDIA SOCIAL NETWORKS TELECOM HEALTH Neo4j Adoption
Users Love Neo4j
15 “Neo4j continues to dominate the graph database market. It’s not surprising but Neo4j rules!” Noel Yuhanna Forrester Market Overview: Graph Database Vendors September 2017
Real-Time Recommendations Dynamic Pricing Artificial Intelligence & IoT-applications Fraud Detection Network Management Customer 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 Integration API ETL SaaS DatabaseTooling Discover&Visualize CUSTOMERS BUSINESS USERS DEVELOPERS ADMINS DATA SCIENTISTS OTHER SYSTEMS APPS AI / ML
Solving Massive-Scale Challenges: Recommendations 19 People, Places, Things + Interests + Transactions + Activity Each requires a new & higher level of scaling
Pros Simple Stops rookies Traditional Fraud Detection: Discrete Data Analysis Revolving Debt INVESTIGATE INVESTIGATE Number of accounts Cons False positives False negatives 20
“Connected Analysis” : next generation Fraud detection Revolving Debt Number of accounts ADVANTAGES Detect 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 of navigation 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 Neo4j Commercial 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 WITH PERSON 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 inappropriate when data relationships are valuable in real-time Slow development Poor performance Low scalability Hard 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 3 3
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 storage Optimized 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 querying Memory 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 ResponseTime Relational and Other NoSQL Databases 0 to 2 hops 0 to 3 degrees Thousands of connections 1000x Advantage Tens to hundreds of hops Thousands of degrees Billions 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 3 7 Social Recommendation Example Neo4j Advantage - Performance
Neo4j Scalability Dynamic pointer compression Unlimited-sized graphs with no performance compromise Index partitioning Auto-partitioning of indexes into 2GB partitions Causal clustering architecture Enables unlimited read scaling with ACID writes and a choice of consistency levels Multi-Data Center Support Creates HA, Fault Tolerant Global Applications Efficient processing Native graph processing and storage often requires 10x less hardware Efficient storage One-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 jeff@neo4j.com 9/19/17
Neo4j Enterprise Edition Native Graph Platform Graph Overview
Neo4j 2.3 Release Summary GA 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 Neo4j Neo4j Application Neo4j New language driversNew binary protocol Improved cost-based query optimizer New file, config and log structure for tomorrow’s deployments Native Language Drivers BOLT New 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 Architecture Modern and Fault-Tolerant to Guarantee Graph Safety 43 Neo4j Advantage – Scalability
Neo4j 3.1 Highlights Security Foundation Database Kernel and Operations Advances 44 IBM Power8 CAPI Flash Support Schema Viewer Causal Clustering State-of-the-Art Cluster Architecture
Highlights of Neo4j 3.2 May 2017 GA Enterprise scale for global applications Continuous improvement in native performance Enterprise governance for 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 Estimated 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 tracing Compiled 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 Enterprise Native Graph Differentiation Graph Overview
Discrete Data Minimally connected data Neo4j is designed for data relationships Neo4j's Connections-First Positioning & Focus Other NoSQL Relational DBMS Neo4j Graph DB Connected Data Focused on Data Relationships Development Benefits Easy model maintenance Easy query Deployment Benefits Ultra high performance Minimal resource usage
Theme: Why Non-Native Graphs Fail Why 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 Language The 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 to replicated 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 Data Q R Q R Relationship Queries on non-native Graph Architectures 5 7
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 Over ACID Consistency Graph Transactions Over Non-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 Integration API ETL SaaS DatabaseTooling Discover&Visualize CUSTOMERS BUSINESS USERS DEVELOPERS ADMINS DATA SCIENTISTS 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 Neo4j Commercial 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 complicated for 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.com online 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 Monitoring66 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 marketing communications • 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 Edition Native Graph Platform Graph Overview

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Neo4j GraphDay Seattle- Sept19- Connected data imperative

  • 1.
    • Connected DataImperative • Graphs-Boosted Artificial Intelligence • Break • Enterprise Ready—Neo4j in Production • Lunch • Neo4j Training • Reception Neo4j Graph Day Seattle
  • 2.
    Connected Data Imperative #1Database for Connected Data Jeff Morris Head of Product Marketing jeff@neo4j.com 9/19/17
  • 3.
    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
  • 4.
    CONSUMER DATA PRODUCT DATA PAYMENT DATA SOCIAL DATA SUPPLIER DATA The next waveof competitive advantage will be all about using connections to produce actionable insights. The Age of Connections
  • 5.
    Discrete Data ProblemsConnected Data Problems Perspective SELECT foo FROM emp SQL (Ann)-[:LOVES]->(Dan) CypherQuery Language RDBMS GRAPH DB DBMS Architectur e
  • 6.
    Graph is TopTrending Database Type
  • 7.
    Neoj4’s Amazing Customers NASAexplores graph database for deep insights into space International Consortium of Investigative Journalists Wins Pulitzer Prize
  • 8.
    Business Problem • Findrelationships 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
  • 9.
    “Graph analysis ispossibly 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, 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 Making Big Data Normal with Graph Analysis for the Masses, 2015 http://www.gartner.com/document/3100219 Analyst Expectations Three Years Ago 9
  • 10.
    The Largest GraphInnovation Network 3,000,000+ with 50k additional per month Neo4j Downloads 250+ customers, 500+ startups 50% 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
  • 11.
    Graph Visionaries Enterprise Customers 11 Partners SystemIntegrators Trainers OEMs Cloud IaaS, PaaSm, DBaaS Marketplace OSS Community Events Forums Add-Ons The Density of the Neo4j Innovation Network Tech Ecosystem OEM & Tech Partners Graph Solutions Data Science Architecture Data Models Commercial Support Technical Support Packaged Services Custom Services Education Documents Online Training Classroom Custom Onsite Standards Initiatives openCypher, LDBS
  • 12.
    October 23 &24 New York City The premier conference for graph technology Speakers Include:
  • 13.
  • 14.
  • 15.
    15 “Neo4j continues todominate the graph database market. It’s not surprising but Neo4j rules!” Noel Yuhanna Forrester Market Overview: Graph Database Vendors September 2017
  • 16.
    Real-Time Recommendations Dynamic Pricing Artificial Intelligence &IoT-applications Fraud Detection Network Management Customer Engagement Supply Chain Efficiency Identity and Access Management Relationship-Driven Applications
  • 17.
    Sample of ConnectedGraphs Organization Identity & Access Network & IT Ops
  • 18.
    Neo4j Graph Platform 18 TransactionsAnalytics Data Integration API ETL SaaS DatabaseTooling Discover&Visualize CUSTOMERS BUSINESS USERS DEVELOPERS ADMINS DATA SCIENTISTS OTHER SYSTEMS APPS AI / ML
  • 19.
    Solving Massive-Scale Challenges: Recommendations 19 People,Places, Things + Interests + Transactions + Activity Each requires a new & higher level of scaling
  • 20.
    Pros Simple Stops rookies Traditional FraudDetection: Discrete Data Analysis Revolving Debt INVESTIGATE INVESTIGATE Number of accounts Cons False positives False negatives 20
  • 21.
    “Connected Analysis” :next generation Fraud detection Revolving Debt Number of accounts ADVANTAGES Detect 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
  • 22.
    Gartner’s Layered FraudPrevention Approach (4) (4) http://www.gartner.com/newsroom/id/1695014 Next Gen Fraud Prevention: Entity Linking Analysis of users and their endpoints Analysis of navigation 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
  • 23.
    Why is Neo4jSucceeding? 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
  • 24.
    The Connected EnterpriseValue 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 Neo4j Commercial Value
  • 25.
    Neo4j Features &Advantages Neo4j Graph Platform
  • 26.
    CAR name: “Dan” born: May29, 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 WITH PERSON PERSON 26 Neo4j Advantage - Agility
  • 27.
  • 28.
    Cypher: Powerful andExpressive Query Language MATCH (:Person { name:“Dan”} ) -[:MARRIED_TO]-> (spouse) MARRIED_TO Dan Ann NODE RELATIONSHIP TYPE LABEL PROPERTY VARIABLE Neo4j Advantage – Developer productivity
  • 29.
    Open Source (Available toanyone) 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
  • 30.
  • 31.
    Relational DBMSs Can’tHandle 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
  • 32.
    Queries can takenon-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 3 3
  • 33.
    At Write Time: datais 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
  • 34.
    Neo4j: Native Graphfrom the Start Native graph storage Optimized 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 querying Memory 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
  • 35.
    Connectedness and Sizeof Data Set ResponseTime Relational and Other NoSQL Databases 0 to 2 hops 0 to 3 degrees Thousands of connections 1000x Advantage Tens to hundreds of hops Thousands of degrees Billions of connections Graph “Minutes to milliseconds” “Minutes to Milliseconds” Real-Time Query Performance
  • 36.
    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 3 7 Social Recommendation Example Neo4j Advantage - Performance
  • 37.
    Neo4j Scalability Dynamic pointercompression Unlimited-sized graphs with no performance compromise Index partitioning Auto-partitioning of indexes into 2GB partitions Causal clustering architecture Enables unlimited read scaling with ACID writes and a choice of consistency levels Multi-Data Center Support Creates HA, Fault Tolerant Global Applications Efficient processing Native graph processing and storage often requires 10x less hardware Efficient storage One-tenth the disk and memory requirements of certain alternatives Neo4j Advantage – Scalability
  • 38.
    in the Enterprise #1Database for Connected Data Jeff Morris Head of Product Marketing jeff@neo4j.com 9/19/17
  • 39.
    Neo4j Enterprise Edition NativeGraph Platform Graph Overview
  • 40.
    Neo4j 2.3 ReleaseSummary GA 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
  • 41.
    Neo4j 3.0: ANew Architecture Foundation 42 Cypher Engine Parser Rule-based optimizer Cost-based optimizer Runtime Neo4j Neo4j Application Neo4j New language driversNew binary protocol Improved cost-based query optimizer New file, config and log structure for tomorrow’s deployments Native Language Drivers BOLT New storage engine with no limits Enterprise Edition Java Stored Procedures
  • 42.
    Raft-based architecture • Continuouslyavailable • 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 Architecture Modern and Fault-Tolerant to Guarantee Graph Safety 43 Neo4j Advantage – Scalability
  • 43.
    Neo4j 3.1 Highlights Security Foundation Database Kerneland Operations Advances 44 IBM Power8 CAPI Flash Support Schema Viewer Causal Clustering State-of-the-Art Cluster Architecture
  • 44.
    Highlights of Neo4j3.2 May 2017 GA Enterprise scale for global applications Continuous improvement in native performance Enterprise governance for the connected enterprise 45 sa group uk group us_east group hk group
  • 45.
    Neo4j Performance Improvementsby 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 Estimated Neo4j 3.3
  • 46.
    Global Iterative GraphAlgorithms 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
  • 47.
    Features in Communityand 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 tracing Compiled 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
  • 48.
    Neo4j Supported Platforms On-PremisePlatforms Cloud Platforms and Containers IBM POWER For Development … and others
  • 49.
    Why Neo4j: KeyTechnology 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
  • 50.
    Shopping Recommendations Examples ofcompanies 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
  • 51.
    Neo4j in theEnterprise Native Graph Differentiation Graph Overview
  • 52.
    Discrete Data Minimally connected data Neo4jis designed for data relationships Neo4j's Connections-First Positioning & Focus Other NoSQL Relational DBMS Neo4j Graph DB Connected Data Focused on Data Relationships Development Benefits Easy model maintenance Easy query Deployment Benefits Ultra high performance Minimal resource usage
  • 53.
    Theme: Why Non-NativeGraphs Fail Why 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
  • 54.
    Cypher: Powerful andExpressive Query Language MATCH (:Person { name:“Dan”} ) -[:MARRIED_TO]-> (spouse) MARRIED_TO Dan Ann NODE RELATIONSHIP TYPE LABEL PROPERTY VARIABLE Neo4j Advantage – Developer productivity
  • 55.
    56 Example HR Queryin 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 Language The query asks: “Find all direct reports and how many people they manage, up to three levels down”
  • 56.
    UNIFIED, IN-MEMORY MAP Lightning-fast queriesdue to replicated 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 Data Q R Q R Relationship Queries on non-native Graph Architectures 5 7
  • 57.
    NoSQL Databases Don’tHandle 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
  • 58.
    Graph Transactions Over ACIDConsistency Graph Transactions Over Non-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
  • 59.
    Neo4j Graph Platform 61 TransactionsAnalytics Data Integration API ETL SaaS DatabaseTooling Discover&Visualize CUSTOMERS BUSINESS USERS DEVELOPERS ADMINS DATA SCIENTISTS OTHER SYSTEMS APPS AI / ML
  • 60.
    The Connected EnterpriseValue 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 Neo4j Commercial Value
  • 61.
  • 62.
    Background • Large PublicUniversity – “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 complicated for 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
  • 63.
    Business Problem • Optimizewalmart.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.com online store • 2M+ employees and $470 billion in annual revenues Walmart RETAIL Real-Time Recommendations65
  • 64.
    Background • Brazil's largestbank, #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 Monitoring66 CE Customer since 2016 Q1EE Customer since Q2 2017
  • 65.
    Background • Large globalbank • 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
  • 66.
    Background • SF-based C2Crental 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
  • 67.
    Background • San Jose-basedcommunications 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
  • 68.
    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
  • 69.
    Background • Large NordicTelecom 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
  • 70.
    Business Problem • Needednew 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
  • 71.
    Business Problem • Neededto 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 marketing communications • Helps clients provide omni-channel experience with in-context information Pitney Bowes MARKETING COMMUNICATIONS Master Data Management73
  • 72.
    Background • World's largesthospitality / 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
  • 73.
    Neo4j Enterprise Edition NativeGraph Platform Graph Overview