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OLTP + OLAP ftw! QCon San Francisco November 8, 2016 ABOUT ME - PowerPoint PPT Presentation

OLTP + OLAP ftw! QCon San Francisco November 8, 2016 ABOUT ME Developed web apps for 5 years including e-commerce, business workflow, more. Worked at Google for 8 years on Google Apps, Cloud Platform Technologies: Python, Java,


  1. OLTP + OLAP ftw! QCon San Francisco November 8, 2016

  2. ABOUT ME • Developed web apps for 5 years including e-commerce, business workflow, more. • Worked at Google for 8 years on Google Apps, Cloud Platform • Technologies: Python, Java, BigQuery, Oracle, MySQL, OAuth ryan@neo4j.com @ryguyrg

  3. Talking about Today • Quick intro to Graph DBs 
 • How Analytics works with Neo4j - then, now, and soon! 
 • Our patriotic duty to combine the two

  4. What is a Graph?

  5. A Graph Is Connected Data

  6. TRAFFIC LIGHTS ROAD A Graph Is Connected Data

  7. AVAILABLE ROOMS HAS AVAILABLE HOTEL A Graph Is Connected Data

  8. What does a Graph look like?

  9. Whiteboard Model is the Physical Model

  10. Graphing Love Loves Ann Dan

  11. Property Graph Model LOVES NODE NODE CREATE (:Person { name:“Ann”} ) - [:LOVES]-> (:Person { name:“Dan”} ) LABEL LABEL PROPERTY PROPERTY

  12. Property Graph Model: Extended name: “Dan” born: May 29, 1970 name: “Ann” twitter: “@dan” born: Dec 5, LOVES LOVES PERSON PERSON LIVES WITH OWN DRIVE since: 
 Jan 10, brand: “Volvo” CAR model: “V70”

  13. GRAPHS FOR OLTP

  14. Intuitivness Speed Agility

  15. What about Neo4j? + CREATE MODEL LOAD DATA QUERY DATA

  16. CYPHER!!!! (Graphs)-[:ARE]->(Everywhere)

  17. How do you Query?

  18. How do you use Neo4j?

  19. O ffi cial Language Drivers

  20. Community Language Drivers

  21. Extending Neo4j

  22. GRAPHS FOR ANALYTICS

  23. (1) DATA IN GRAPH (3) RESULTS IN GRAPH (4) QUERY GRAPH (2) ANALYZE RELATIONSHIPS

  24. GraphX Spark’s API for graph processing

  25. MAZERUNNER FLOW

  26. NEO4J SPARK CONNECTOR

  27. Neo4j Spark Connector CYPHER over BOLT

  28. APOC Library

  29. APOC Library CYPHER over BOLT

  30. GRAPHS FOR RECOMMENDATIONS

  31. The main product Recommendations (In Real-Time) Related products People who bought X also bought Y

  32. KITCHEN AID SERIES S _ A T L O O K

  33. Emails Tweets KITCHEN AID SERIES reviews Complaints S _ A T L O O K

  34. Emails Tweets Returns KITCHEN AID SERIES reviews Complaints S _ A T L O O K

  35. Emails Tweets Returns KITCHEN AID SERIES reviews Complaints S _ A T L O O K Inventory

  36. Emails Tweets Returns KITCHEN AID SERIES reviews Complaints S _ A T L O O K Inventory Promotions Express goods Home delivery Bundling Location/

  37. Emails Tweets Returns KITCHEN AID SERIES reviews Complaints Price-range Purchase History S _ A T L O O K Category Inventory Promotions Express goods Home delivery Bundling Location/

  38. Emails Tweets Returns KITCHEN AID SERIES reviews Complaints Price-range Purchase History S _ A T L O O K Category Inventory Promotions Express goods Home delivery Bundling Location

  39. Simple Recommendations

  40. Content Filtering: Article Recommendations

  41. Content Filtering: Article Recommendations

  42. Content Filtering: Product Recommendations

  43. Content Filtering: Product Recommendations

  44. Our Patriotic Duty

  45. Legislative Graph

  46. Determining Influence

  47. Determining Influence

  48. Executing PageRank

  49. Most Influential Senators

  50. Most Influential Representatives

  51. 
 Recommend to Me: 
 Member of Congress who can get me a Tesla everyone ^

  52. Subject Matter Experts

  53. Combining Analytics + Subgraph Traversal

  54. Other Graph Algorithms • Community Detection 
 • Centrality (Betweenness, Closeness) 
 • aStar (djikstra with h(0))

  55. Betweenness Centrality

  56. Community Detection

  57. graphdatabases.com

  58. THANK YOU! Ryan Boyd @ryguyrg ryan@neo4j.com

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