Understanding Graph Technologies

[Deze workshop sluit aan op het congres Datawarehousing & BI Summit waar Thomas Frisendal optreedt. U kunt zich voor deze workshop apart aanmelden via deze pagina of, met korting, in combinatie met het congres via het registratieformulier van het congres.]

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).