Get To Know The Real World: Discovering Connected Data with a Graph Database

Find out what a graph database is and how it can transform your applications and data! We will explore creating, querying, and displaying data and learn how to use simple tools to interact with the database. We will cover the whiteboard-friendly model and the basics of the Cypher query language. Learn how graph databases can improve the data world!

Transcript

1. Get to Know the Real World…
   Discovering Connected Data with a Graph Database
   Jennifer Reif
   Neo4j
   @JMHReif
   

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2. Who Am I?
   • Developer Relations Engineer for Neo4j
   
   • Continuous learner
   
   • Conference speaker
   
   • Blogger
   
   • Hobbies: cats, coffee, traveling
   Email: jennifer.reif@neo4j.com
   Twitter: @JMHReif
   

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3. What is a Graph Database?
   

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4. Graph
   Chart
   

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5. Database - specifically graph
   • Database: a structured set of data held in a computer,
   especially one that is accessible in various ways.
   • Relational? NoSQL? Graph?
   
   • Graph database: uses graph structures for semantic
   queries with nodes, edges and properties to represent
   and store data.
   

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6. Why would I choose graph?
   

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7. The world is a graph – everything is connected
   • people, places, events
   
   • companies, markets
   
   • countries, history, politics
   
   • sciences, art, teaching
   
   • technology, networks, machines, 

   applications, users
   
   • software, code, dependencies, 

   architecture, deployments
   
   • criminals, fraudsters, and their
   behavior
   

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9. Relational
   Graph
   

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10. Other NoSQL Graph
    

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11. What is it used to accomplish?
    Internal Applications
    • Master Data Management
    
    • Network and 

    IT Operations
    
    • Fraud Detection
    
    Customer-Facing Applications
    • Real-Time Recommendations
    
    • Graph-Based Search
    
    • Identity and 

    Access Management
    

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12. What is it used to accomplish?
    Use Cases
    • Social networks
    
    • Impact analysis
    
    • Logistics and routing
    
    • Recommendations
    
    • Access control
    
    • Fraud analysis
    
    • …and many, many more!
    

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13. How do you do it?
    

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14. Whiteboard
    Friendliness
    Easy to design and model direct representation of the model
    

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15. Whiteboard friendliness
    

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16. Whiteboard friendliness
    The Matrix
    Cloud Atlas
    Tom Hanks
    ACTED_IN
    Lana
    Wachowski
    DIRECTED
    DIRECTED
    Hugo Weaving
    ACTED_IN ACTED_IN
    

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17. Whiteboard friendliness
    title: Cloud Atlas
    released: 2012
    title: The Matrix
    released: 1999
    Movie
    Movie
    name: Tom Hanks
    born: 1956
    ACTED_IN
    roles: Zachry
    Person Actor
    name: Lana Wachowski
    born: 1965
    DIRECTED
    DIRECTED
    Person Director
    ACTED_IN
    roles: Bill Smoke
    ACTED_IN
    roles: Agent Smith
    name: Hugo Weaving
    born: 1960
    Person Actor
    

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18. Whiteboard friendliness
    

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19. Graph Data Model
    

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20. Property Graph Data Model
    • 2 Main Components:
    • Nodes
    • Relationships
    • Additional Components:
    • Labels
    
    • Properties
    

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21. Property Graph Data Model
    • Nodes:
    • Represent the objects in the graph
    • Can be categorized using Labels
    Car
    Person Person
    

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22. Property Graph Data Model
    • Nodes:
    • Represent the objects in the graph
    • Can be categorized using Labels
    • Relationships:
    • Relate nodes by type and direction
    Car
    DRIVES
    LOVES
    LOVES
    LIVES WITH
    OW
    NS
    Person Person
    

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23. Property Graph Data Model
    • Nodes:
    • Represent the objects in the graph
    • Can be categorized using Labels
    • Relationships:
    • Relate nodes by type and direction
    
    • Properties:
    • Name-value pairs that can be applied
    to nodes or relationships
    Car
    DRIVES
    LOVES
    LOVES
    LIVES WITH
    OW
    NS
    Person Person
    name: “Dan”
    born: May 29, 1970
    twitter: “@dan”
    name: “Ann”
    born: Dec 5, 1975
    since:
    Jan 10, 2011
    brand: “Volvo”
    model: “V70”
    

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24. Cypher Query Language….
    SQL for graphs
    

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25. Cypher: Powerful and Expressive
    CREATE (:Person { name:“Dan”}) -[:LOVES]-> (:Person { name:“Ann”})
    LOVES
    Dan Ann
    LABEL PROPERTY
    NODE NODE
    LABEL PROPERTY
    

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26. Cypher: Powerful and Expressive
    LOVES
    Dan Ann
    MATCH (:Person { name:"Dan"} ) -[:LOVES]-> ( whom ) 

    RETURN whom
    

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27. Importing Data to Neo4j
    

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28. Options for Importing Data
    • Cypher statements / script: create individual statements
    to load data manually.
    • LOAD CSV: used for small and medium data sets can
    import local or online csv files to graph.
    
    • ETL Tool: can import from a relational database and maps
    relational data model to graph.
    
    • APOC: standard library that includes several import
    procedures for different data formats
    

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29. //Load Movie objects that are wanted
    WITH 'https://api.themoviedb.org/3/search/movie?api_key='+
    $apiKey+'&query=Lord%20of%20the%20Rings' as url
    CALL apoc.load.json(url)
    YIELD value
    UNWIND value.results AS results
    WITH results
    MERGE (m:Movie {movieId: results.id})

    ON CREATE SET m.title = results.title, m.desc = results.overview, m.poster =
    results.poster_path, m.reviewStars = results.vote_average, m.reviews = results.vote_count
    WITH results, m
    CALL apoc.do.when(results.release_date = "",
    'SET m.releaseDate = null',
    'SET m.releaseDate = date(results.release_date)',
    {m:m, results:results}) YIELD value
    RETURN m
    

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30. //For Movie objects just loaded, pick out trilogy and retrieve cast of those movies
    WITH 'https://api.themoviedb.org/3/movie/' as prefix, '/credits?api_key='+$apiKey as suffix,
    ["The Lord of the Rings: The Fellowship of the Ring", "The Lord of the Rings: The Two Towers",
    "The Lord of the Rings: The Return of the King"] as movies
    CALL apoc.periodic.iterate('MATCH (m:Movie) WHERE m.title IN $movies RETURN m',
    'WITH m CALL apoc.load.json($prefix+m.movieId+$suffix) YIELD value
    UNWIND value.cast AS cast
    MERGE (c:Cast {id: cast.id})
    ON CREATE SET c.name = cast.name
    MERGE (ch:Character {name: cast.character})
    MERGE (ch)-[r:APPEARS_IN]->(m)
    MERGE (c)-[r1:PLAYED]->(ch)',
    {batchSize: 1, iterateList:false, params:{movies:movies, prefix:prefix, suffix:suffix}});
    

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31. Demo Time!!
    

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32. Resources
    • Neo4j download: https://neo4j.com/download/
    
    • Neo4j sandbox: https://neo4j.com/sandbox-v2/
    
    • Neo4j guides: https://neo4j.com/developer/get-started
    
    • Cypher: https://neo4j.com/developer/cypher/
    
    • LOAD CSV: https://neo4j.com/developer/guide-import-csv/
    
    • APOC: https://neo4j-contrib.github.io/neo4j-apoc-procedures/
    
    • Neo4j Certification: https://neo4j.com/graphacademy/neo4j-certification/
    @JMHReif
    jennifer.reif@neo4j.com
    

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