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GraphTalks Rome - Introducing Neo4j

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GraphTalks Rome March 2017
Bill Brooks, Neo Technology

Publié dans : Technologie
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GraphTalks Rome - Introducing Neo4j

  1. 1. Introducing  Neo4j The  Graph  Database Bill  Brooks Sales  Manager
  2. 2. What is the most powerful database in the world?
  3. 3. Tables?
  4. 4. A  Graph
  5. 5. What  is  not  a  Graph
  6. 6. Vertice Edge What  is  a  Graph
  7. 7. Graph  Theory Meet Leonhard Euler • Swiss  mathematician • Inventor  of  Graph   Theory  (1736)
  8. 8. Königsberg  (Prussia)  -­‐ 1736
  9. 9. A B D C
  10. 10. A B D C 1 2 3 4 7 6 5
  11. 11. A B D C 1 2 3 4 7 6 5
  12. 12. Labeled  Property  Graph  Data  Model
  13. 13. 2000                  2003                                2007      2009   2011 2013 2014 20152012 Neo4j:  The  Graph  Database  Leader GraphConnect,   first  conference   for  graph  DBs First   Global  2000   Customer Introduced   first  and  only   declarative  query   language  for   property  graph Published  O’Reilly   book on  Graph   Databases $11M  Series  A   from  Fidelity,   Sunstone and  Conor $11M  Series  B   from  Fidelity,   Sunstone and  Conor Commercial Leadership First native   graph  DB in  24/7   production Invented   property   graph   model Contributed   first  graph   DB  to  open   source $2.5M  Seed Round from   Sunstone   and  Conor Funding Extended   graph  data   model  to   labeled  property   graph 150+  customers 50K+  monthly downloads 500+  graph   DB  events worldwide   $20M  Series  C   led  by   Creandum,  with   Dawn  and   existing  investors Technical Leadership
  14. 14. “Forrester  estimates  that  over  25%  of  enterprises  will  be  using   graph  databases  by  2017” Neo4j  Leads  the  Graph  Database  Revolution “Neo4j  is  the  current  market  leader  in  graph  databases.” “Graph  analysis  is  possibly  the  single  most  effective  competitive   differentiator for  organizations  pursuing  data-­‐driven  operations   and  decisions  after  the  design  of  data  capture.” IT  Market  Clock   for  Database   Management   Systems,  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 Graph   Databases   – and   Their  Potential   to  Transform   How   We  Capture   Interdependencies   (Enterprise   Management   Associates) http://blogs.enterprisemanagement.com/dennisdrogseth/2013/11/06/graph-­‐databasesand-­‐potential-­‐transform-­‐capture-­‐interdependencies/
  15. 15. Largest  Ecosystem  of  Graph  Enthusiasts • 1,000,000+  downloads • 20,000+  education  registrants • 18,000+  Meetup  members • 100+  technology  and  service   partners • 150+  enterprise  subscription   customers   including  50+  Global  2000   companies
  16. 16. High  Business  Value  in  Data  Relationships Data  is  increasing  in  volume… • New  digital  processes • More  online  transactions • New  social  networks • More  devices Using  Data  Relationships  unlocks  value   • Real-­‐time  recommendations • Fraud  detection • Master  data  management • Network  and  IT  operations • Identity  and  access  management • Graph-­‐based  search …  and  is  getting  more  connected Customers,   products,  processes,  devices  interact   and  relate  to  each  other Early  adopters  became  industry  leaders
  17. 17. Relational  DBs  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
  18. 18. 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
  19. 19. The  Relational  Crossroads
  20. 20. Neo4j  – Re-­‐Imagine  Your  Data  as  a  Graph Neo4j  is  an  enterprise-­‐grade  graph   database  that  enables  you  to: • Model  and  store your  data  as  a   graph • Query  data  relationships with   ease  and  in  real-­‐time • Seamlessly  evolve  applications  to   support  new  requirements  by   adding  new  kinds  of  data  and   relationships Agile  development High  performance Vertical  and  horizontal  scale Seamless  evolution
  21. 21. The  Whiteboard  Model  Is the  Physical  Model
  22. 22. Key  Neo4j  Product  Features Native  Graph  Storage Ensures  data  consistency  and  performance Native  Graph  Processing Millions  of  hops  per  second,  in  real  time “Whiteboard  Friendly”  Data  Modeling Model  data  as  it  naturally  occurs High  Data  Integrity Fully  ACID  transactions Powerful,  Expressive  Query  Language Requires  10x  to  100x  less  code  than  SQL Scalability  and  High  Availability Vertical  and  horizontal  scaling  optimized  for   graphs Built-­‐in  ETL Seamless  import  from  other  databases Integration Drivers  and  APIs  for  popular  languages MATCH (A)
  23. 23. NEO4j USE CASES Real Time Recommendations Meta Data Management Fraud Detection Identity & Access Management Graph Based Search Network & IT-Operations Logistik RD BM S CRM RD BM S Mails Ma ils yst Dokume nteFil es ys em Media   LibraryFil es ys em CMS RD BM S Social RD BM S LogFiles RD BM S Ecommer ce RD BM S IN
  24. 24. Master  Data  Management
  25. 25. Adidas Meta Data Management Shared  Meta  Data  Service Background • Global leader in sporting goods industry services firm footware, apparel, hardware, 14.5 bln sales, 53,000 people • Multitude of products, markets, media, assets and audiences Business Problem • Beset by a wide array of information silos including data about products, markets, social media, master data, digital assets, brand content and more • Provide the most compelling and relevant content to consumers • Offering enhanced recommendations to drive revenue Solution and Benefits • Save time and cost through stadardized access to content sharing-system with internal teams, partners, IT units, fast, reliable, searchable avoiding reduandancy • Inprove customer experience and increase revenue by providing relevant content and recommentations
  26. 26. Background • Mid-size German insurer founded in 1858 • Project executed by Delvin, a subsidiary of die Bayerische Versicherung and an IT insurance specialist Business Problem • Field sales needed easy, dynamic, 24/7 access to policies and customer data • Existing DB2 system unable to meet performance and scaling demands Solution and Benefits • Enabled flexible searching of policies and associated personal data • Raised the bar on industry practices • Delivered high performance and scalability • Ported existing metadata easily Die Bayerische INSURANCE Master  Data  Management31
  27. 27. Network  Impact  Analysis
  28. 28. Background • Second largest communications company in France • Based in Paris, part of Vivendi Group, partnering with Vodafone Solution and Benefits • Flexible inventory management supports modeling, aggregation, troubleshooting • Single source of truth for entire network • New apps model network via near-1:1 mapping between graph and real world • Schema adapts to changing needs Network  and  IT  Operations SFR COMMUNICATIONS Business Problem • Infrastructure maintenance took week to plan due to need to model network impacts • Needed what-if to model unplanned outages • Identify network weaknesses to uncover need for additional redundancy • Info lived on 30+ systems, with daily changes LINKED LINKED DEPENDS_ON Router Service Switch Switch Router Fiber  Link Fiber  Link Fiber  Link Oceanfloor   Cable 33
  29. 29. Recommendations
  30. 30. 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  Recommendations35
  31. 31. Logistics
  32. 32. Background • One of the world’s largest logistics carriers • Projected to outgrow capacity of old system • New parcel routing system Single source of truth for entire network B2C and B2B parcel tracking Real-time routing: up to 7M parcels per day Business Problem • Needed 365x24x7 availability • Peak loads of 3000+ parcels per second • Complex and diverse software stack • Need predictable performance, linear scalability • Daily changes to logistics network: route from any point to any point Solution and Benefits • Ideal domain fit: a logistics network is a graph • Extreme availability, performance via clustering • Greatly simplified routing queries vs. relational • Flexible data model reflect real-world data variance much better than relational • Whiteboard-friendly model easy to understand Accenture LOGISTICS 37 Real-­‐Time  Routing  Recommendations
  33. 33. Access  Control
  34. 34. Background • Top investment bank with $1+ trillion in assets • Using a relational database and Gemfire to manage employee permissions to research document and application-service resources • Permissions for new investment managers and traders provisioned manually Business Problem • Lost an average of 5 days per new hire while they waited to be granted access to hundreds of resources, each with its own permissions • Replace an unsuccessful onboarding process implemented by a competitor • Regulations left no room for error Solution and Benefits • Store models, groups and entitlements in Neo4j • Exceeded performance requirements • Major productivity advantage due to domain fit • Graph visualization ease permissioning process • Fewer compromises than with relational • Expanded Neo4j solution to online brokerage London Investment Bank FINANCIAL SERVICES Identity  and  Access  Management39
  35. 35. Background • Oslo-based telcom provider is #1 in Nordic countries and #10 in world • Online, mission-critical, self-serve system lets users manage subscriptions and plans • availability and responsiveness is critical to customer satisfaction Business Problem • Logins took minutes to retrieve relational access rights • Massive joins across millions of plans, customers, admins, groups • Nightly batch production required 9 hours and produced stale data Solution and Benefits • Shifted authentication from Sybase to Neo4j • Moved resource graph to Neo4j • Replaced batch process with real-time login response measured in milliseconds that delivers real-time data, vw yday’s snapshot • Mitigated customer retention risks Identity  and  Access  Management Telenor COMMUNICATIONS SUBSCRIBED_BY CONTROLLED_BY PART_OFUSER_ACCESS Account Customer CustomerUser Subscription 40
  36. 36. Fraud  Analysis
  37. 37. Background • Global financial services firm with trillions of dollars in assets • Varying compliance and governance considerations • Incredibly complex transaction systems, with ever-growing opportunities for fraud Business Problem • Needed to spot and prevent fraud detection in real time, especially in payments that fall within “normal” behavior metrics • Needed more accurate and faster credit risk analysis for payment transactions • Needed to dramatically reduce chargebacks Solution and Benefits • Lowered TCO by simplifying credit risk analysis and fraud detection processes • Identify entities and connections uniquely • Saved billions by reducing chargebacks and fraud • Enabled building real-time apps with non-uniform data and no sparse tables or schema changes London and New York Financial FINANCIAL SERVICES Fraud  Detection s 42
  38. 38. Background • Panama based lawyers Mossack & Fonseca do business in hosting “letterbox companies” • Suspected to support tax saving and organized crime • Altogether: 2.6 TB, 11 milo files, 214.000 letter box companies Business Problem • Goal to unravel chains Bank-Person–Client– Address–Intermediaries – M&F • Earlier cases: spreadsheet based analysis (back- and-forth) & pencil to extract such connections • This case: sheer amount of data & arbitrarily chain length condemn such approaches to fail Solution and Benefits • 400 journalists, investigate/update/share, 2 people with IT background • Identify connections quickly and easily • Fast Results wouldn‘t be possible without GraphDB Panama Papers Fraud Detection Fraud  Detection43
  39. 39. Gracias

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