Slides from GraphDay Santa Clara
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Graphs in Action: Slide 3 Under the Hood: What Graphs are and Where They Fit -- Slide 35 Transform Your Data from RDBMS to Graph: A Worked Example -- Jump to slide 82
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Slides from GraphDay Santa Clara
- 1. SANTA CLARA APRIL 14, 2016 09:00-09:30 09:30-10:15 10:15-11:00 11:00-11:30 11:30-12:30 12:30-13:30 13:30-17:00 Breakfast and Registration Graphs in Action: Driving Digital Transformation with Neo4j Under the Hood: What’s a Graph and Where Do They Fit Break Transform Your Data: A worked example Lunch Training Session Agenda
- 2. Speakers Lars Nordwall Emil Eifrem Kevin Van Gundy Nicole White
- 3. Driving Digital Transformation With Neo4j GRAPHS IN ACTION Santa Clara, April 14, 2016 Lars Nordwall Chief Operating Officer @lnordwall lars@neotechnology.com
- 5. Corporate Threat Source: Accenture Strategy research, summer 2015 700 business leaders in the European Union, United States, China and Japan, a majority identified large digital players or start-ups as the greatest competitive threat to profitable growth. *)
- 6. Corporate Life Span The average corporate life span has been falling for more than half a century. Standard & Poor’s data show it was - 61 years in 1958 - 25 years in 1980 - 18 years in 2011 Digitization is placing unprecedented pressure on organizations to evolve. At the present rate, 75 percent of S&P 500 incumbents will be gone by 2027 Source: McKinsey, 2015
- 7. Dilemma Everyone collects data today. More data the better.. “Store first, ask questions later” Everyone seems to hire data scientists today (or at least trying).
- 8. There is another dimension beyond data volume: Data Relationships Why a graph database?
- 9. Social networks RetailHR & Recruiting Manufacturing & Logistics Health Care Telco Today we see graph-projects in virtually every industry Finance
- 10. Retail
- 11. Neo4j solves retail-related challenges for some of the largest companies in the world 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 Now Tackles eCommerce Delivery Service Routing with Neo4j “We needed to rebuild when growth and new features made our slowest query longer than our fastest delivery - 15 minutes! Neo4j gave us best solution” – Volker Pacher, 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
- 12. End Consumers Component Manufacturers Logistics Traditional Retail Value Chain RetailersWholesalers Assembly Plants
- 13. PAYMENTS SALES- CHANNELS SUPPLY CHAIN PRODUCTS MARKETING CRM CUSTOMER EXPERIENCE The Online Retail Value Chain
- 15. PAYMENTS SALES- CHANNELS SUPPLY CHAIN PRODUCTS MARKETING CRM CUSTOMER EXPERIENCE Store Mobile Shipping Inventory Express goods Home delivery Webstore
- 16. PAYMENTS SALES- CHANNELS SUPPLY CHAIN PRODUCTS MARKETING CRM CUSTOMER EXPERIENCE Store Mobile Shipping Inventory Express goods Home delivery Ratings Price-range Category Webstore
- 17. PAYMENTS SALES- CHANNELS SUPPLY CHAIN PRODUCTS MARKETING CRM CUSTOMER EXPERIENCE Store Mobile Shipping Inventory Express goods Home delivery Ratings Price-range Category Content Promotions Online advertising Webstore
- 18. PAYMENTS SALES- CHANNELS SUPPLY CHAIN PRODUCTS MARKETING CRM CUSTOMER EXPERIENCE Store Mobile Shipping Inventory Express goods Home delivery Ratings Price-range Category Content Promotions Online advertising Loyalty Programs Returns Feedback reviews Tweets Emails Customer support Webstore
- 19. PAYMENTS SALES- CHANNELS SUPPLY CHAIN PRODUCTS MARKETING CRM CUSTOMER EXPERIENCE Store Mobile Shipping Inventory Express goods Home delivery Ratings Price-range Category Content Promotions Online advertising Loyalty Programs Returns Feedback reviews Tweets Emails Customer support Credit Card Cash Mobile Pay Purchase History PAYMENTS Webstore
- 20. Digital transformation in retail today requires to put all this data into good use
- 23. Related products People who bought X also bought Y Recommendations (In Real-Time) The main product
- 28. LOOKS_AT Returns Home delivery Inventory Express goods Complaints reviews Tweets Emails Location/ KITCHEN AID SERIES Promotions Bundling
- 29. LOOKS_AT Returns Purchase History Price-range Home delivery Inventory Express goods Complaints reviews Tweets Emails Category Promotions Bundling Location/ KITCHEN AID SERIES
- 30. LOOKS_AT Returns Purchase History Price-range Home delivery Inventory Express goods Complaints reviews Tweets Emails Category Promotions Bundling Location KITCHEN AID SERIES
- 33. To get results, in real time, from a dataset that is highly interconnected – you need a graph database!
- 34. THANK YOU! Lars Nordwall Chief Operating Officer @lnordwall lars@neotechnology.com
- 35. Under the Hood: What’s a Graph, and Where Do They Fit Santa Clara, April 14, 2016 Emil Eifrem CEO, Neo Technology Founder, Neo4j
- 36. What is the most powerful database in the world?
- 39. The internet
- 40. Genetic Ancestry of One Single Corn Variety
- 42. GOT IT. GRAPHS. BUT WHAT IS A GRAPH?
- 46. PERFORMED PAUL McCARTNEY BEATLES A Graph Is BELONGS_TO SINGER COMPOSER HEY JUDE
- 50. Use of Graphs has created some of the most successful companies in the world C 34,3%B 38,4%A 3,3% D 3,8% 1,8% 1,8% 1,8% 1,8% 1,8% E 8,1% F 3,9%
- 51. NEO4j USE CASES Real Time Recommendations Master Data Management Fraud Detection Identity & Access Management Graph Based Search Network & IT-Operations
- 52. NEO4j USE CASES Real Time Recommendations Master Data Management Fraud Detection Identity & Access Management Graph Based Search Network & IT-Operations VIEWED GRAPH THINKING: Real Time Recommendations VIEWED BOUGHT VIEWED BOUGHT BOUGHT BOUGHT BOUGHT
- 53. “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 Wada Software Developer, Walmart NEO4j USE CASES Real Time Recommendations Master Data Management Fraud Detection Identity & Access Management Graph Based Search Network & IT-Operations
- 54. NEO4j USE CASES Real Time Recommendations Master Data Management Fraud Detection Identity & Access Management Graph Based Search Network & IT-Operations GRAPH THINKING: Master Data Management MANAGES MANAGES LEADS REGION M ANAG ES MANAGES REGION LEADS LEADS COLLABORATES
- 55. Neo4j is the heart of Cisco HMP: used for governance and single source of truth and a one-stop shop for all of Cisco’s hierarchies. NEO4j USE CASES Real Time Recommendations Master Data Management Fraud Detection Identity & Access Management Graph Based Search Network & IT-Operations
- 56. NEO4j USE CASES Real Time Recommendations Master Data Management Fraud Detection Identity & Access Management Graph Based Search Network & IT-Operations O PENED_ACCO UNT HAS IS_ISSUED GRAPH THINKING: Fraud Detection HAS LIVES LIVES IS_ISSUED OPENED_ACCOUNT
- 57. “Graph databases offer new methods of uncovering fraud rings and other sophisticated scams with a high-level of accuracy, and are capable of stopping advanced fraud scenarios in real-time.” Gorka Sadowski Cyber Security Expert NEO4j USE CASES Real Time Recommendations Master Data Management Fraud Detection Identity & Access Management Graph Based Search Network & IT-Operations
- 58. GRAPH THINKING: Graph Based Search PUBLISH INCLUDE INCLUDE CREATE CAPTURE IN IN SOURCE USES USES IN IN USES NEO4j USE CASES Real Time Recommendations Master Data Management Fraud Detection Identity & Access Management Graph Based Search Network & IT-Operations SOURCE SOURCE
- 59. Uses Neo4j to manage the digital assets inside of its next generation in-flight entertainment system. NEO4j USE CASES Real Time Recommendations Master Data Management Fraud Detection Identity & Access Management Graph Based Search Network & IT-Operations
- 60. NEO4j USE CASES Real Time Recommendations Master Data Management Fraud Detection Identity & Access Management Graph Based Search Network & IT-Operations BROWSES CONNECTS BRIDGES ROUTES POWERS ROUTES POWERS POWERS HOSTS QUERIES GRAPH THINKING: Network & IT-Operations
- 61. Uses Neo4j for network topology analysis for big telco service providers NEO4j USE CASES Real Time Recommendations Master Data Management Fraud Detection Identity & Access Management Graph Based Search Network & IT-Operations
- 62. GRAPH THINKING: Identity And Access Management TRUSTS TRUSTS ID ID AUTHENTICATES AUTHENTICATES NEO4j USE CASES Real Time Recommendations Master Data Management Fraud Detection Identity & Access Management Graph Based Search Network & IT-Operations O W NS OWNS CAN_READ
- 63. UBS was the recipient of the 2014 Graphie Award for “Best Identify And Access Management App” NEO4j USE CASES Real Time Recommendations Master Data Management Fraud Detection Identity & Access Management Graph Based Search Network & IT-Operations
- 64. Neo4j Adoption by Selected Verticals SOFTWARE FINANCIAL SERVICES RETAIL MEDIA & BROADCASTING SOCIAL NETWORKS TELECOM HEALTHCARE
- 65. TECHNICAL BENEFITS OF GRAPH DATABASES
- 68. Intuitivness
- 70. 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 Neo4j “Minutes to milliseconds” Real-Time Query Performance
- 71. Speed “We found Neo4j to be literally thousands of times faster than our prior MySQL solution, with queries that require 10-100 times less code. Today, Neo4j provides eBay with functionality that was previously impossible.” - Volker Pacher, Senior Developer “Minutes to milliseconds” performance Queries up to 1000x faster than RDBMS or other NoSQL
- 73. A Naturally Adaptive Model A Query Language Designed for Connectedness + =Agility
- 74. Cypher Typical Complex SQL Join 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
- 75. CYPHER
- 77. openCypher
- 79. Impact on the Business Neo4j is ultra efficient & normally needs far less hardware than any alternative How? Increase revenue • Do new & impossible things • Faster time-to-market Reduce cost • Lower infrastructure costs How? • Value from data relationships • Batch to real time • 1000x faster
- 80. THANK YOU!
- 81. Coffee BreakNext session: Transform Your Data: A Worked Example
- 82. TRANSFORM YOUR DATA Santa Clara, April 15, 2016 Neo4j @ GraphDay
- 85. "The future is now…"
- 86. ACCOUNT HOLDER 2 ACCOUNT HOLDER 1 ACCOUNT HOLDER 3 CREDIT CARD BANK ACCOUNT BANK ACCOUNT BANK ACCOUNT ADDRESS PHONE NUMBER PHONE NUMBER SSN 2 UNSECURE LOAN SSN 2 UNSECURE LOAN CREDIT CARD
- 88. AGENDA • SQL Pains • Building a Neo4j Application • Moving from RDBMS -> Graph Models • Walk through an Example • Creating Data in Graphs • Querying Data
- 89. SQL Day in the Life of a RDBMS Developer
- 97. SELECT p.name, c.country, c.leader, p.hair, u.name, u.pres, u.state FROM people p LEFT JOIN country c ON c.ID=p.country LEFT JOIN uni u ON p.uni=u.id WHERE u.state=‘CT’
- 102. Have you seen Ted's UUID?
- 104. • Complex to model and store relationships • Performance degrades with increases in data • Queries get long and complex • Maintenance is painful SQL Pains
- 105. • Easy to model and store relationships • Performance of relationship traversal remains constant with growth in data size • Queries are shortened and more readable • Adding additional properties and relationships can be done on the fly - no migrations Graph Gains
- 106. SQL Pains
- 107. Graph Gains
- 108. SQL Pains
- 109. Graph Gains
- 113. How do you use Neo4j? CREATE MODEL + LOAD DATA QUERY DATA
- 114. How do you use Neo4j?
- 116. How do you use Neo4j?
- 117. Language Drivers
- 118. Language Drivers
- 120. Architectural Options Data Storage and Business Rules Execu5on Data Mining and Aggrega5on Applica'on Graph Database Cluster Neo4j Neo4j Neo4j Ad Hoc Analysis Bulk Analy'c Infrastructure Hadoop, EDW … Data Scien'st End User Databases Rela5onal NoSQL Hadoop
- 122. FROM RDBMS TO GRAPHS
- 125. Northwind
- 126. Northwind - the canonical RDBMS Example
- 127. ( )-[:TO]->(Graph)
- 130. Starting with the ER Diagram
- 131. Locate the Foreign Keys
- 132. Drop the Foreign Keys
- 133. Find the JOIN Tables
- 134. (Simple) JOIN Tables Become Relationships
- 135. Attributed JOIN Tables -> Relationships with Properties
- 136. Querying a Subset Today
- 137. As a Graph
- 138. QUERYING THE GRAPH
- 139. using openCypher
- 140. Who do people report to? MATCH (sub:Employee)-[:REPORTS_TO]->(e:Employee) RETURN *
- 141. Who do people report to?
- 142. Who do people report to? MATCH (sub:Employee)-[:REPORTS_TO]->(e:Employee) RETURN e.employeeID AS managerID, e.firstName AS managerName, sub.employeeID AS employeeID, sub.firstName AS employeeName;
- 143. Who do people report to?
- 144. Who does Robert report to? MATCH p=(sub:Employee)-[:REPORTS_TO]->(e:Employee) WHERE sub.firstName = ‘Robert’ RETURN p
- 145. Who does Robert report to?
- 146. What is Robert’s reporting chain? MATCH p=(sub:Employee)-[:REPORTS_TO*]->(e:Employee) WHERE sub.firstName = ‘Robert’ RETURN p
- 147. What is Robert’s reporting chain?
- 148. Report: Product Cross-Selling MATCH (o:Order)-[:INCLUDES]->(:Product{productName:'Chocolade'}), (employee)-[:SOLD]->(o), (employee)-[:SOLD]->(otherOrder)-[:INCLUDES]->(other:Product) RETURN employee.firstName, other.productName, COUNT(DISTINCT otherOrder) as count ORDER BY count DESC;
- 150. POWERING AN APP
- 151. Simple App
- 152. Simple Python Code
- 153. Simple Python Code
- 154. Simple Python Code
- 155. Simple Python Code
- 156. But how do I liberate my RDBMs data?
- 157. CSV
- 158. CSV files for Northwind
- 159. 3 Steps to Creating the Graph IMPORT NODES CREATE INDEXES IMPORT RELATIONSHIPS
- 160. Importing Nodes // Create categories USING PERIODIC COMMIT LOAD CSV WITH HEADERS FROM "https:// raw.githubusercontent.com/neo4j-contrib/developer-resources/ gh-pages/data/northwind/categories.csv" AS row CREATE (:Category {categoryID: row.CategoryID, categoryName: row.CategoryName, description: row.Description}); // Create orders USING PERIODIC COMMIT LOAD CSV WITH HEADERS FROM "https:// raw.githubusercontent.com/neo4j-contrib/developer-resources/ gh-pages/data/northwind/orders.csv" AS row MERGE (order:Order {orderID: row.OrderID}) ON CREATE SET order.shipName = row.ShipName;
- 161. Importing Nodes // Create customers USING PERIODIC COMMIT LOAD CSV WITH HEADERS FROM "https:// raw.githubusercontent.com/neo4j-contrib/developer-resources/ gh-pages/data/northwind/customers.csv" AS row CREATE (:Customer {companyName: row.CompanyName, customerID: row.CustomerID, fax: row.Fax, phone: row.Phone}); // Create products USING PERIODIC COMMIT LOAD CSV WITH HEADERS FROM "https:// raw.githubusercontent.com/neo4j-contrib/developer-resources/ gh-pages/data/northwind/products.csv" AS row CREATE (:Product {productName: row.ProductName, productID: row.ProductID, unitPrice: toFloat(row.UnitPrice)});
- 162. Creating Indexes CREATE CONSTRAINT ON (p:Product) ASSERT p.productID is UNIQUE; CREATE CONSTRAINT ON (e:Employee) ASSERT e.employeeID is UNIQUE; CREATE CONSTRAINT ON (c:Customer) ASSERT c.customerID is UNIQUE; CREATE INDEX ON :Product(productName); CREATE INDEX ON :Category(categoryID); CREATE INDEX ON :Supplier(supplierID); CREATE INDEX ON :Customer(customerName);
- 163. Sew it together…
- 164. Creating Relationships USING PERIODIC COMMIT LOAD CSV WITH HEADERS FROM "https:// raw.githubusercontent.com/neo4j-contrib/developer-resources/ gh-pages/data/northwind/orders.csv" AS row MATCH (order:Order {orderID: row.OrderID}) MATCH (customer:Customer {customerID: row.CustomerID}) MERGE (customer)-[:PURCHASED]->(order); USING PERIODIC COMMIT LOAD CSV WITH HEADERS FROM "https:// raw.githubusercontent.com/neo4j-contrib/developer-resources/ gh-pages/data/northwind/products.csv" AS row MATCH (product:Product {productID: row.ProductID}) MATCH (supplier:Supplier {supplierID: row.SupplierID}) MERGE (supplier)-[:SUPPLIES]->(product);
- 165. Creating Relationships USING PERIODIC COMMIT LOAD CSV WITH HEADERS FROM "https://raw.githubusercontent.com/neo4j- contrib/developer-resources/gh-pages/data/northwind/orders.csv" AS row MATCH (order:Order {orderID: row.OrderID}) MATCH (product:Product {productID: row.ProductID}) MERGE (order)-[pu:INCLUDES]->(product) ON CREATE SET pu.unitPrice = toFloat(row.UnitPrice), pu.quantity = toFloat(row.Quantity); USING PERIODIC COMMIT LOAD CSV WITH HEADERS FROM "https://raw.githubusercontent.com/neo4j- contrib/developer-resources/gh-pages/data/northwind/orders.csv" AS row MATCH (order:Order {orderID: row.OrderID}) MATCH (employee:Employee {employeeID: row.EmployeeID}) MERGE (employee)-[:SOLD]->(order);
- 166. Creating Relationships USING PERIODIC COMMIT LOAD CSV WITH HEADERS FROM "https://raw.githubusercontent.com/ neo4j-contrib/developer-resources/gh-pages/data/northwind/ products.csv" AS row MATCH (product:Product {productID: row.ProductID}) MATCH (category:Category {categoryID: row.CategoryID}) MERGE (product)-[:PART_OF]->(category); USING PERIODIC COMMIT LOAD CSV WITH HEADERS FROM "https://raw.githubusercontent.com/ neo4j-contrib/developer-resources/gh-pages/data/northwind/ employees.csv" AS row MATCH (employee:Employee {employeeID: row.EmployeeID}) MATCH (manager:Employee {employeeID: row.ReportsTo}) MERGE (employee)-[:REPORTS_TO]->(manager);
- 167. High Performance LOADing neo4j-import 4.58 million things and their relationships… Loads in 100 seconds!
- 168. WRAPPING UP
- 169. “Graph analysis is possibly the single most effective competitive differentiator for organizations pursuing data-driven operations and decisions after the design of data capture.”
- 170. THANK YOU! Kevin Van Gundy @kevinvangundy kevin@neo4j.com