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Introduction to the Neo4j Graph Platform & use cases

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Dirk Möller - Neo4j

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Introduction to the Neo4j Graph Platform & use cases

  1. 1. Gob Summit - Madrid #1 Database for Connected Data Dirk Möller Director Sales CEMEA dirk@neo4j.com 19/11/2019
  2. 2. Neo4j GraphTalks Network & Application Management • Einführung in Graphdatenbanken und Neo4j (9.30-10.00) Bruno Ungermann • Neue Herangehensweisen für Network und Application Mgt mit Graphen (10.00-11.00) Stefan Kolmar • Wie werden Graphdatenbank-Projekte mit Neo4j zum Erfolg? (11.00-11.30) Stefan Kolmar • Q&A
  3. 3. Agenda • Impact of Graphs • State of the Graph • Three waves • What‘s enabling all of this?
  4. 4. ACCOUNT ADDRESS PERSON PERSON NAME STREET BANK NAME COMPANY BANK BAHAMAS 2.6 TB 11.5 million documents Emails, Scanned Documents, Bank Statements etc…
  5. 5. 2.6 TB 11.5 million documents Emails, Scanned Documents, Bank Statements etc… Person B Bank US Account 123 Person A Acme Inc Bank Bahama s Address XNODE RELATIONSHIP
  6. 6. 2.6 TB 11.5 million documents Emails, Scanned Documents, Bank Statements etc…
  7. 7. ICIJ Pulitzer Price Winner 2017
  8. 8. State of the graph
  9. 9. “Forrester estimates that over 25% of enterprises will be using graph databases by 2017.” Forrester, 2014
  10. 10. Popularity of Graphs DB-engines Ranking of Database Categories • Graph DBMS • Key-value stores • Document stores • Wide column store • RDF stores • Time stores • Native XML DBMS • Object oriented DBMS • Multivalue DBMS • Relational DBMS Graph DB 2013 2014 2015 2016 2017 2018
  11. 11. Software Financial Services Telecom Retail & Consumer Goods Media & Entertainment Other Industries Airbus Over 300 Enterprises and 10s of Thousands of Projects on Neo4j
  12. 12. 7 of the Top 10 Software Companies Use Neo4j
  13. 13. 8 of the Top 10 Insurance Companies Use Neo4j
  14. 14. Category Defining Use Cases airbnb Fraud Detection Real-Time Recommendations Network & IT Operations Master Data Management Knowledge Graph Identity & Access Management
  15. 15. 10M+ Downloads 3M+ from Neo4j Distribution 7M+ from Docker Events 400+ Approximate Number of Neo4j Events per Year 50k+ Meetups Number of Meetup Members Globally Largest pool of graph technologists 50k+ Trained/certified Neo4j professionals Trained Developers
  16. 16. 2012  2018 May 10th-11th, London CONFERENCE + TRAINING
  17. 17. of enterprises are using graph databases As of today Source: Forrester Vendor Landscape: Graph Databases, October 6, 2017
  18. 18. "Neo4j continues to dominate the graph database market.” “69% of enterprises have, or are planning to implement graphs over next 12 months” October, 2017 “The most widely stated reason in the survey for selecting Neo4j was to drive innovation” February, 2018 Critical Capabilities for DBMSA “In fact, the rapid rise of Neo4j and other graph technologies may signal that data connectedness is indeed a separate paradigm from the model consolidation happening across the rest of the NoSQL landscape.” March, 2018 Graph is a Unique Paradigm
  19. 19. Three waves
  20. 20. Our core belief is — connections between data are as important as the data itself
  21. 21. Reveal connections? Look at this data
  22. 22. Look at the same data as a graph
  23. 23. Graph Based Success
  24. 24. Retail 7 of top 10 Finance 20 of top 25 7 of top 10 Software Hospitality 3 of top 5 Telco 4 of top 5 Airlines 3 of top 5 Logistics 3 of top 5 76% FORTUNE 100 have adopted or piloted Neo4j
  25. 25. 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
  26. 26. 37 • Record “Cyber Monday” sales • About 35M daily transactions • Each transaction is 3-22 hops • Queries executed in 4ms or less • Replaced IBM Websphere commerce • 300M pricing operations per day • 10x transaction throughput on half the hardware compared to Oracle • Replaced Oracle database • Large postal service with over 500k employees • Neo4j routes 7M+ packages daily at peak, with peaks of 5,000+ routing operations per second. Handling Large Graph Work Loads for Enterprises Real-time promotion recommendations Marriott’s Real-time Pricing Engine Handling Package Routing in Real-Time
  27. 27. Home Security Internet of things Institutional Memory Entertainment Recommendations Home Operations Personalization Voice Enabled Smart Home
  28. 28. More Data Enables More Use Cases
  29. 29. Data Network Effect “A product, generally powered by machine learning, becomes smarter as it gets more data from your users. The more users use your product, the more data they contribute; the more data they contribute, the smarter your product becomes.” — Matt Turck
  30. 30. What’s Enabling All of This?
  31. 31. A year ago…
  32. 32. 43 Neo4j Graph Platform Development & Administration Analytics Tooling BUSINESS USERS DEVELOPERS ADMINS Graph Analytics Graph Transactions Data Integration Discovery & Visualization DATA ANALYSTS DATA SCIENTISTS Drivers & APIs APPLICATIONS AI
  33. 33. Neo4j Bloom
  34. 34. Case Studies Some examples from our customers
  35. 35. CONTEXTO PATRONES PERSPECTIVA PRESENTACIÓN BY GRAPH EVERYWHERE
  36. 36. Graph Database Relational Database Una forma de representar información Muy Flexible Consultas en tiempo real Altamente contextual Estructurado Pre-calculado Basado en reglas rígidas PRESENTACIÓN BY GRAPH EVERYWHERE
  37. 37. Grafos: Las redes sociales Scoring de recomendación de personas PRESENTACIÓN BY GRAPH EVERYWHERE
  38. 38. RELACIONAL: AMIGOS DE MIS AMIGOS PRESENTACIÓN BY GRAPH EVERYWHERE
  39. 39. a b Cypher: Perfecto para patrones c PRESENTACIÓN BY GRAPH EVERYWHERE
  40. 40. NEO4J: AMIGOS DE MIS AMIGOS Pedro Josep Encontrar amigos de amigos en una red social, hasta una profundidad máxima de cinco niveles. Red de 1.000.000 de personas Cada persona tiene unos 50 amigos aprox. Bea Gregor Cristina PRESENTACIÓN BY GRAPH EVERYWHERE
  41. 41. NEO4J: MAS ALLA DE LA SIMPLICIDAD Nivel Profund Relacional Tiempo (seg) Neo4j Tiempo (seg) Registros Devueltos 2 0.016 0.01 ⋍ 2.500 3 30.267 0.168 ⋍ 110.000 4 1543.505 1.359 ⋍ 600.000 5 No Termina 2.132 ⋍ 800.000 *Partner y Vukotic’s Experiment - Graph Databasesby Ian Robinson, Jim Webber, and Emil Eifrem PRESENTACIÓN BY GRAPH EVERYWHERE
  42. 42. Informes para los jueces: ORGA MEJORA SOLUCIÓN 3 OBJETIVO l RETO Q TIEMPO IMPLEMENTACIÓN w PRESENTACIÓN BY GRAPH EVERYWHERE
  43. 43. PRESENTACIÓN BY GRAPH EVERYWHERE
  44. 44. Análisis crimen organizado RETO Q SOLUCIÓN 3 OBJETIVO l TIEMPO IMPLEMENTACIÓN w PRESENTACIÓN BY GRAPH EVERYWHERE
  45. 45. Fraude en concesiones de subvenciones RETO Q SOLUCIÓN 3 MEJORA l TIEMPO IMPLEMENTACIÓN w PRESENTACIÓN BY GRAPH EVERYWHERE
  46. 46. Fraude interno RETO Q SOLUCIÓN 3 MEJORA l TIEMPO IMPLEMENTACIÓN w PRESENTACIÓN BY GRAPH EVERYWHERE
  47. 47. CONTEXTO PATRONES PERSPECTIVA PRESENTACIÓN BY GRAPH EVERYWHERE

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