Graph Databases: Solving Problems with Connected Data

In this connected world, traditional data stores often make it difficult to find valuable relationships. Graph databases focus not only on the entities, but the connections between them. By making relationships a key component of the model, contextualizing a set of data becomes incredibly simple. Join us in this session to learn how graph databases are used to improve the data world and help developers easily make sense of connected data!

Topics:
* Exploring graphs and how they provide data context
* How to transform our projects into graph structures
* Optimizing the model for our use case
* Accessing data through queries, analysis, visualization, applications, and more!

Transcript

1. Jennifer Reif
   Email: jennifer.reif@neo4j.com
   Twitter: @JMHReif
   LinkedIn: linked.com/in/jmhreif
   Github: github.com/JMHReif
   Website: jmhreif.com
   Graph Databases
   Solving Problems with Connected Data
   

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2. Who Am I?
   • Developer + Advocate
   
   • Continuous learner
   
   • Blogger
   
   • Speaker
   
   • Other: geek
   Jennifer Reif
   Email: jennifer.reif@neo4j.com
   Twitter: @JMHReif
   LinkedIn: linked.com/in/jmhreif
   Github: github.com/JMHReif
   Website: jmhreif.com
   

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3. It’s all about data
   Everything is built on data
   • Businesses, industry, governments, economics, people
   
   • Our understanding of the world
   
   • Data is how we learn
   
   • Trial and error
   
   • Asking questions
   
   • Accumulated over time
   

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4. Domain-Driven Design (DDD)
   • Mutual understanding of domain, concepts
   
   • Iterations
   
   • Understanding data = better understanding of result
   
   • Better product, minimum functionality
   
   • Sanity check code
   

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5. Databases - another one?!
   

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6. Relational database
   • Pros:
   
   • Slices of data that can be easily assembled with queries
   
   • Handles data integrity and unique rows, low data duplication
   
   • Cons:
   
   • All relationships must be assembled with JOINs (with NF adherence)
   
   • JOINs are constructed from human knowledge
   Goal: Assemble data into meaningful sets
   

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7. Entity-Relationship Diagram
   

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8. NoSQL/Document database
   • Pros:
   
   • Can handle varied (unstructured) data more readily
   
   • Groups related info in a single place
   
   • Cons:
   
   • Designed to avoid JOINs and SQL
   
   • Makes aggregation operations cumbersome
   Goal: Group related info together
   

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9. NoSQL/Document Schema
   

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10. Graph database
    Goal: Best of both worlds
    • Pros:
    
    • Relationships stored with related entities, no assembly required
    
    • Make JOIN operations easy, visual even
    
    • Cons:
    
    • Storing relationships requires some write operation overhead
    
    • Di
    ff
    erent mentality for modeling, querying, etc
    

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11. Graph databases
    

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12. Have to pick just one?
    • Polyglot persistence:
    
    • Using multiple persistence strategies, each serving a di
    ff
    erent need
    
    • “Using the right tool for the job”
    

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13. What is a graph?
    

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14. Graph History
    Leonard Euler
    Seven Bridges of Konigsberg problem. Leonhard Euler, 1735
    

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15. Graph databases 2010
    New tech arises from…
    • Solution need
    
    • Missing solution in the technology space
    
    • Looking for a “better way” to handle certain problems
    

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16. What Does It Solve?
    Product use case
    • Document path (not just data)
    
    • Answering how and why
    
    • Understanding connections
    
    • Find alternates, impacts, etc.
    
    • Produce better assumptions, decisions
    

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17. Graphs add context and meaning
    Not just point-to-point, but HOW they connect
    

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18. Misconceptions
    “Gotchas”
    • Database - physical storage of data
    
    • Not just analysis tool
    
    • Not just visualization
    
    • Can be “system of record” / “source of truth” db
    
    • Not just analytics workloads, transactional too
    
    • Not a silver bullet (like any technology)
    
    • There are things it does NOT do well
    

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19. Graph Structure
    

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

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21. Data model drawn = real-world data model
    

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22. Data model drawn = data stored in graph
    

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23. Graph components
    • Node (vertex)
    
    • Relationship (edge)
    

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24. Nodes
    Graph Components
    • Represent objects or entities
    
    • Can be labeled
    Order Product
    

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25. Nodes
    Graph components
    • Represent objects or entities
    
    • Can be labeled
    
    • May have properties
    Order Product
    Employee
    orderId: 162468
    
    
    total: $8320.48
    productId: 08746589
    
    
    name: “Ipoh Coffee”
    id: 247924
    
    
    startDate: 2016-05-04
    
    
    role: “Salesperson”
    

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26. Relationships
    Graph components
    • Must have a type
    
    • Must have a direction
    Order Product
    Employee
    orderId: 162468
    
    
    

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27. Relationships
    Graph components
    • Must have a type
    
    • Must have a direction
    
    • May have properties
    Order Product
    Employee
    orderId: 162468
    
    
    commission: .02
    

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28. Relationships
    Graph components
    • Must have a type
    
    • Must have a direction
    
    • May have properties
    
    • Nodes can share multiple
    relationships
    Order Product
    Employee
    orderId: 162468
    
    
    

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29. Label
    Graph Components
    • A group of nodes
    
    • Like a category
    Person
    

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30. Label
    Graph Components
    • A group of nodes
    
    • Like a category
    Person
    Employee
    Customer
    

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31. Property
    Graph components
    • Key-value pair
    
    • Associated with node or relationship
    
    • Like a variable name and value
    Order
    Employee
    orderId: 162468
    id: 247924
    SOLD
    total: $8320.48
    date: 2022-08-16
    commission: .02
    startDate: 2016-05-04
    role: “Salesperson”
    

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32. Query Language
    

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33. SQL vs Cypher
    SELECT o.OrderId, o.Total, o.Items, op.Quantity,
    
    
    op.Price, p.Name
    
    
    FROM Order o
    
    
    LEFT JOIN OrderedProduct op ON op.OrderId = o.OrderId
    
    
    LEFT JOIN Product p ON op.ProductId = p.ProductId;
    SQL
    Cypher
    MATCH (o:Order)-[r:CONTAINS]->(p:Product)
    
    
    RETURN o, r, p;
    Order Product
    OrderedProduct
    Order Product
    

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34. Cypher
    • Functional and visual
    
    • Based on ASCII-art
    
    • Declarative query language
    
    • Focuses on what to retrieve
    
    • Not how
    A B
    LIKES
    MATCH (A)-[:LIKES]->(B)
    

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35. Cypher: powerful and expressive
    Jennifer Neo4j
    WORKS_FOR
    CREATE (:Person { name: ‘Jennifer’}) -[:WORKS_FOR]-> (:Company { name: ‘Neo4j’})
    NODE
    PROPERTY
    NODE
    PROPERTY
    LABEL LABEL
    

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36. Cypher: read
    Jennifer Neo4j
    WORKS_FOR
    MATCH (:Person { name: ‘Jennifer’} ) -[:WORKS_FOR]-> ( whom )
    

    RETURN whom
    

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37. Cypher in 20 seconds…
    • Nodes look like this:
    
    • (var:Label) OR (var:Label {propKey: propValue})
    
    • Relationships look like this:
    
    • -[var:REL_TYPE]-> OR -[var:REL_TYPE {propKey: propValue}]->
    
    • Using Cypher is looking for patterns of those nodes/rels:
    
    • (var1:Label)-[var2:REL_TYPE]->(var3:Label)
    

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38. Building Applications
    

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39. Building applications
    • Drivers: lots of languages supported
    
    • Plugins/extensions/libraries:
    
    • APOC utilities
    
    • Graph Data Science
    
    • GraphQL
    
    • …and more!
    
    • Integrations/frameworks:
    
    • Spring, Micronaut, Quarkus
    
    • Kettle, Spark, PowerBI, JDBC, etc.
    

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40. Demo!
    

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41. Resources
    • Neo4j AuraDB Free: dev.neo4j.com/aura-java
    
    • Neo4j GraphAcademy: dev.neo4j.com/learn
    
    • Neo4j blog: dev.neo4j.com/blog
    
    • Neo4j YouTube: dev.neo4j.com/videos
    Jennifer Reif
    Email: jennifer.reif@neo4j.com
    Twitter: @JMHReif
    LinkedIn: linked.com/in/jmhreif
    Github: github.com/JMHReif
    Website: jmhreif.com
    

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