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![(Michael)*[:WORKS_FOR]*>(Neo4j)
michael@neo4j.com |?@mesirii?|?github.com/jexp?|?jexp.de/blog
Michael?Hunger??* Caretaker?General?@Neo4j](https://image.slidesharecdn.com/graphspastpresentfuture-170331125528/85/Graphs-Neo4j-Past-Present-Future-3-320.jpg)
![Graph&Query&Language:&Cypher
(jan:Person {,name:“Jan”},) 3[:LOVES]3>,(anne:Person {,name:“Anne”},)
LOVES
Jan Anne
NODE NODE
LABEL PROPERTYLABEL PROPERTY
MATCH](https://image.slidesharecdn.com/graphspastpresentfuture-170331125528/85/Graphs-Neo4j-Past-Present-Future-31-320.jpg)
![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
Find%all%reports%and%how%many%
people%they%manage,%
up%to%3%levels%down
Cypher'Query
SQL'Query](https://image.slidesharecdn.com/graphspastpresentfuture-170331125528/85/Graphs-Neo4j-Past-Present-Future-32-320.jpg)
Uploaded byjexp
Graphs & Neo4j - Past Present Future
The document discusses the evolution and future of graph databases, particularly focusing on Neo4j, including its adoption across various industries and the benefits of graph analysis. It highlights the growing trend of organizations using graph databases for competitive advantages and operational efficiencies. The document also explores the development of Neo4j's features, community engagement, and predictions for future advancements in graph technology.
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Graphs & Neo4j - Past Present Future
- 1. Graphs & Neo4j Past- Present - Future Meetup Sept 27, Stockholm - NetEnt
- 2. Michael Hunger Caretaker GeneralNeo4j Community
- 3.
- 8. KNOWS KNOWS NAME: ANN AGE: 32 SINCE:2012 KNOWS NODE PROPERTY RELATIONSHIPS
- 9. KNOWS KNOWS KNOWS NAME: EVE AGE: 32 SINCE:2012 KNOWS SELL VIEWS SELL PRICE:10$ PALO ALTO PRICE:5$ OAKLAND SELL
- 12.
- 13. “Graph analysis isthe true killer app for Big Data.” “Forrester estimates that over 25% of enterprises will be using graph databases by 2017.” Today It Is An Industry
- 14. “Graph analysis ispossibly the single most effective competitive differentiator for organizations pursuing data-driven operations and decisions after the design of data capture.” “By the end of 2018, 70% of leading organizations will have one or more pilot or proof-of-concept efforts underway utilizing graph databases.” Today It Is An Industry
- 15. Neo4j Adoption bySelected Verticals SOFTWARE FINANCIAL SERVICES RETAIL MEDIA & OTHER SOCIAL NETWORKS TELECOM HEALTHCARE
- 19. How did weget here?
- 20. Let‘s build a, 1.database 2. query language 3. billion $,company how hard can it be?
- 21.
- 23.
- 26.
- 27.
- 31. Graph&Query&Language:&Cypher (jan:Person {,name:“Jan”},) 3[:LOVES]3>,(anne:Person{,name:“Anne”},) LOVES Jan Anne NODE NODE LABEL PROPERTYLABEL PROPERTY MATCH
- 32. MATCH&(boss)%[:MANAGES*0..3]%>(sub), (sub)%[:MANAGES*1..3]%>(report) WHERE&boss.name?=?“John?Doe” RETURN sub.name ASSubordinate,? count(report)?AS Total Find%all%reports%and%how%many% people%they%manage,% up%to%3%levels%down Cypher'Query SQL'Query
- 34. THE IMPACT OFGRAPHS How Graphs Are Changing The World
- 35.
- 37. Biggest Leak inHistory Panama Papers Offshore Leaks Database Snowden – NSA WikiLeaks Cablegate Pentagon Papers 0 5M 10M Documents Leaked
- 41. Unraveling the CancerCode with Graphs
- 44.
- 45. Anybody with anidea and a purpose, can now use a technology that is powerful enough to change an industry
- 46.
- 49.
- 50. Real-Time Recommendations Fraud Detection Network & IT Operations MasterData Management Graph-Based Search Identity & Access Management Sustainable Competitive Advantage With Graphs
- 51. WHAT’S NEXT? Building TheGraph Database For The Future
- 52. What Users Loveabout Neo4j Design Whiteboard friendliness Develop Deploy Great query language Native graph performance
- 53. What Users WantMore Of Design Whiteboard friendliness Develop Deploy Great query language Native graph performance Productivity Developer graph applications faster Scale Design bigger and faster graph apps Operations Deploy graph applications anywhere easily
- 55.
- 57. 2016 - :3.x — Official Language Drivers 0.x — Embedded Java API2000 - 2010: 2.x — Cypher2014 - 2015: 2010 - 2014: 1.x — REST
- 58. Example - Javawith Neo4j 2.x Official Language Drivers
- 59. Official Language Drivers Driverdriver = GraphDatabase.driver( "bolt://localhost" ); try ( Session session = driver.session() ) { StatementResult result = session.run("MATCH (u:User) RETURN u.name"); } With Java
- 60. With JavaScript var driver =Graph.Database.driver("bolt://localhost"); var session = driver.session(); var result = session.run("MATCH (u:User) RETURN u.name"); Official Language Drivers
- 61. With Python driver = Graph.Database.driver("bolt://localhost") session= driver.session() result = session.run("MATCH (u:User) RETURN u.name") Official Language Drivers
- 62. Official Language Drivers using(var driver = GraphDatabase.Driver("bolt://localhost")) using (var session = driver.Session()) { var result = session.Run("MATCH (u:User) RETURN u.name"); } With .NET
- 63. JavaScript Java .NETPython Community Drivers Drivers Bolt PHP, Go, Elixir, Ruby
- 64. Neo4j OGM Spring DataNeo4j 4 Browser.Next Spark Driver JDBC Driver Enabling:
- 65.
- 66.
- 67. Java Stored Procedure @Procedure publicStream<StringResult> uuid() { return Stream.of(new StringResult(UUID.randomUUID().toString())); }
- 68. @Procedure public Stream<StringResult> uuid(){ return Stream.of(new StringResult(UUID.randomUUID().toString())); } Cypher Java Stored Procedure
- 73. RDF / LinkedData Parallelization Meta Data Utilities Data Integration Date & Time Graph Algorithms Enabling: Spatial
- 74.
- 75.
- 76.
- 77. Configuration Log Files Startup Behavior DirectoryStructure Consistency
- 78.
- 80. Scalability Performance The InherentTension Between Scalability And Performance
- 81.
- 82. Huge Networks of: People Devices Software Genes& Proteins Behaviors Documents Enabling:
- 83. Drivers User Defined Procedures UnlimitedSize Graphs
- 84. Future These are mypersonal predictions - Stay Tuned -
- 85. Core-Edge Clustering Enterprise GradeSecurity User Defined Functions 3.1 beta
- 86. Large Scale Clusters Basisfor Sharding Spark Co-Location Compute Instances Auto-Scaling Core Edge
- 87. LDAP Active Directory default roles finegrained roles procedure security later: Kerberos data level security Security
- 88. data conversion date &time json data structures business rules decomposition User Defined Functions
- 89. Graph Compute, Cypheron X Sharding Compiled Runtime 3.2 4.0
- 90.
- 91. Age of Graphs andGraph Databases
- 93.
- 94. Questions? ;-) Ask MeAnything