Graphs are everywhere! In my efforts to spread the word and inform SQL Server Professionals about Neo4j and learn how to take a relational database schema and model it as a graph… AdventureWorks was a no-brainer! Let’s explore how to import a subset of the AdventureWorks database from SQL Server (RDBMS) into Neo4j (GraphDB).

But first some prerequisites. You should at least have a basic understanding of what is a property graph model and have completed the following modeling guidelines. Download and install Neo4j to be able to follow along with the examples.

The AdventureWorks database is a fictitious company database that has existed since SQL Server 2005 as a means to show new functionality in each new version released. AdventureWorks has become a vital aspect of learning new features/constructs within SQL Server.


For our demonstration I have decided to choose a subset of tables and select only offline sales transactions (OnlineOrderFlag = 0).
As a result the following CSV files will be used to import data into our graph structure:

  • customers.csv
  • employees.csv
  • orders.csv
  • productcategories.csv
  • products.csv
  • producsubcategories.csv
  • vendorproduct.csv
  • vendors.csv
  • You can find all the necessary code and files here ->

    If you still need to download a copy of the AdventureWorks sample database (I used the SQL Server 2014 version) you can find them here ->

    Developing a Graph Model
    When deriving and defining a graph model from a relational model, you should keep the following base guidelines in mind:

  • A row is a node
  • A table name is a label name
  • For further understanding read the following GraphDB vs. RDBMS. This article explores the differences between relational and graph databases and data models. In addition, it explains how to integrate graph databases with relational databases and how to import data from a relational store.

    With this dataset, this simple AdventureWorks graph data model serves as a foundation:


    The key difference between a graph and relational database is that relational databases work with sets while graph databases work with paths and relationships are first-class entities in a graph database and are semantically far stronger than those implied relationships reified at runtime in a relational store.

    Importing the Data using Cypher

    Now that we have extracted data from the AdventureWorks database, will be using Cypher’s LOAD CSV command to transform and load the content of these CSV files into a graph structure.

    First we create specific indexes and constraints

    Schema await – now create nodes:

    Create relationships

    You can also import and run the entire Cypher script using the neo4j-shell: bin/neo4j-shell -path adventure.db -file cypher/import.cypher

    Once completed you will be able query and do stuff like this:


    Querying the Graph with Cypher

    Let’s issue some cypher queries to our newly created graph database:




    Explore, learn Cypher and write your own queries

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