Understanding Graph Technologies

Since Google announced its Knowledge Graph solution in 2012 graph database technologies have found their way into many organizations and companies. The graph database market has exploded over the last 10 years with at least 50 brand names today. International Standardization is coming – very soon SQL will be extended by functionality for property graph queries. A full international standard for property graphs, called GQL, will surface in late 2023 (from the same ISO committee that maintains the SQL standard).

Graph databases are generally quite easy to understand – the paradigm is intuitive and seems straightforward. In spite of that, the breadth and power of the solutions, one can create, are overwhelmingly impressive. The inclusion of graph technology dramatically enlarges the scope of analytics by enabling semi-structured information, semantic sources such as ontologies and taxonomies, social networks as well as schema-less sources of data.
At the same time graph databases are much better suited for doing complex multi-joins analyzing large networks of data, opening up for advanced fraud detection etc. The Panama papers is the best-known example.

Finally graph theory is a mathematical discipline with a long history, which among other things have created graph algorithms for many complex analytics, such as clustering, shortest path, page rank, centrality and much more.

Learning Objectives

  • Understand graph parlance and paradigms
  • Understand the principles of graph data modeling
  • Understand “schema on read” approaches and use cases
  • Investigate examples on the database language level
  • Get a feel for the scope of graph solutions
  • Get an overview of the vendors and technologies
  • Get an understanding of the tools available
  • Get a good feel for investigative analytics, graph algorithms and graphs in the ML context
  • Get advice on how to get to play with graph tools
  • Get references to good resources.


Who is it for?

  • People, who architect, design and manage analytical solutions, looking for additional analytics power for complex business concerns
  • People, who implement analytics
  • People, who use analytics applications, tools and data to resolve business issues
  • People, who have some experience with database query languages and/or query tools
  • Business analysts
  • Data and IT consultants.

Although code examples (in graph database query languages) will be used frequently, the audience is not expected to be proficient database developers (but even SQL experts will benefit from the workshop).


Workshop Course Outline

  • Graph Models
    • Graph Theory, Property Graphs and data paradigms
    • Graph models compared to classic (relational) models
    • Schema less, first, last or eventually
    • The Flight Data Model as a property graph
  •  Graph Queries
    • Graph traversals and paths
    • Query languages, incl. international standards work in progress and a market overview
    • Loading, modifying and deleting Data
    • Profiling graph data
  •  Graph Analytics
    • Investigative analytics (Cypher examples)
    • Graph Algorithms
    • Graphs and Machine Learning
  • Best Practices
  • Resources
    • Literature
    • Websites
    • Getting started with a prototype.


It is a somewhat technical workshop, focusing on what and how, using examples. Business and architectural level information can be found in the knowledge graph session on the DW&BI Summit on April 4th.