Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

GraphTalk Hamburg - Einführung in Graphdatenbanken und Neo4j

Neo4j GraphTalk Hamburg
Dirk Möller, Neo4j

  • Login to see the comments

  • Be the first to like this

GraphTalk Hamburg - Einführung in Graphdatenbanken und Neo4j

  1. 1. Graph Talk Hamburg #1 Database for Connected Data Dirk Möller Director Sales CEMEA dirk@neo4j.com 23/10/19
  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? • Use Cases
  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. REAL-TIME RECOMMENDATIONS ML-BASED COMMERCE TRACKING MULTI-DIMENSIONAL SOCIAL ACTIVITY TRANSFORMING CUSTOMER SELF-SERVICE PERSONALIZATION ENGINES Over 200 Customers, Including some of the World’s Largest Companies
  36. 36. Case Study: Knowledge Graphs at eBay
  37. 37. Case Study: Knowledge Graphs at eBay
  38. 38. Case Study: Knowledge Graphs at eBay
  39. 39. Case Study: Knowledge Graphs at eBay Bags
  40. 40. Men’s Backpack Handbag Case Study: Knowledge Graphs at eBay
  41. 41. Case studySolving real-time recommendations for the World’s largest retailer. Challenge • In its drive to provide the best web experience for its customers, Walmart wanted to optimize its online recommendations. • Walmart recognized the challenge it faced in delivering recommendations with traditional relational database technology. • Walmart uses Neo4j to quickly query customers’ past purchases, as well as instantly capture any new interests shown in the customers’ current online visit – essential for making real-time recommendations. Use of Neo4j “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 • With Neo4j, Walmart could substitute a heavy batch process with a simple and real-time graph database. Result/Outcome
  42. 42. Case studyeBay Now Tackles eCommerce Delivery Service Routing with Neo4j Challenge • The queries used to select the best courier for eBays routing system were simply taking too long and they needed a solution to maintain a competitive service. • The MySQL joins being used created a code base too slow and complex to maintain. • eBay is now using Neo4j’s graph database platform to redefine e-commerce, by making delivery of online and mobile orders quick and convenient. Use of Neo4j • With Neo4j eBay managed to eliminate the biggest roadblock between retailers and online shoppers: the option to have your item delivered the same day. • The schema-flexible nature of the database allowed easy extensibility, speeding up development. • Neo4j solution was more than 1000x faster than the prior MySQL Soltution. Our Neo4j solution is literally thousands of times faster than the prior MySQL solution, with queries that require 10-100 times less code. Result/Outcome – Volker Pacher, eBay
  43. 43. Top Tier US Retailer Case studySolving Real-time promotions for a top US retailer Challenge • Suffered significant revenues loss, due to legacy infrastructure. • Particularly challenging when handling transaction volumes on peak shopping occasions such as Thanksgiving and Cyber Monday. • Neo4j is used to revolutionize and reinvent its real-time promotions engine. • On an average Neo4j processes 90% of this retailer’s 35M+ daily transactions, each 3-22 hops, in 4ms or less. Use of Neo4j • Reached an all time high in online revenues, due to the Neo4j-based friction free solution. • Neo4j also enabled the company to be one of the first retailers to provide the same promotions across both online and traditional retail channels. “On an average Neo4j processes 90% of this retailer’s 35M+ daily transactions, each 3-22 hops, in 4ms or less.” – Top Tier US Retailer Result/Outcome
  44. 44. 57 https://www.slideshare.net/neo4j/volvo-cars-build-a-car-with-neo4j
  45. 45. 58 https://www.slideshare.net/neo4j/generali-fraud-analytics
  46. 46. 59 https://www.slideshare.net/neo4j/daimler-ag-structurecube-die-gklasse-im-sumpf-der-strukturen
  47. 47. 60 https://www.slideshare.net/neo4j/graphtour-madrid-running-neo4j-on-a-large-scale-image-platform
  48. 48. Use cases on https://neo4j.com/use-cases/
  49. 49. A Highly Connected Future
  50. 50. Your Homework - Connect

×