Fourth session: Graph Databases versus SQL for Data Science: Identifying 'Graph-y' Problems in Your Data
Links:
Repository: https://dev.neo4j.com/graphy_problems
Slides: https://github.com/cj2001/graphy_problems/blob/main/workshop_slides.pdf
Sandbox: https://dev.neo4j.com/try
Bite-Sized Neo4j for Data Scientists: https://www.youtube.com/watch?v=Niys6g6NFfw&list=PL9Hl4pk2FsvVShoT5EysHcrs-hyCsXaWC
Follow Clair: https://twitter.com/CJLovesData1
0:00 Welcome
8:00 Intro to Graph Data Science
50:00 Q&A Intermezzo
1:01:35 Graph Data Science Hands-on
1:57:10 Summary and Closing
Description
A frequent question from data scientists is "why would I want to use a graph database when I can do all of what I need to do in SQL?" In some cases an RDBMS is a fine solution. However, there are many times that your data is a graph, even though it might not be immediately recognizable as such. This course walks through how to identify whether a problem is actually a graph and the benefits of analyzing that way rather than traditional using traditional SQL.