A Practical Introduction To Graph Data Applications

Episode • Aug 4, 2020 • 1h 0m

Summary

Finding connections between data and the entities that they represent is a complex problem. Graph data models and the applications built on top of them are perfect for representing relationships and finding emergent structures in your information. In this episode Denise Gosnell and Matthias Broecheler discuss their recent book, the Practitioner’s Guide To Graph Data, including the fundamental principles that you need to know about graph structures, the current state of graph support in database engines, tooling, and query languages, as well as useful tips on potential pitfalls when putting them into production. This was an informative and enlightening conversation with two experts on graph data applications that will help you start on the right track in your own projects.

Announcements

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Your host is Tobias Macey and today I’m interviewing Denise Gosnell and Matthias Broecheler about the recently published practitioner’s guide to graph data

Interview

Introduction
How did you get involved in the area of data management?
Can you start by explaining what your goals are for the Practitioner’s Guide To Graph Data?
- What was your motivation for writing a book to address this topic?
What do you see as the driving force behind the growing popularity of graph technologies in recent years?
What are some of the common use cases/applications of graph data and graph traversal algorithms?
- What are the core elements of graph thinking that data teams need to be aware of to be effective in identifying those cases in their existing systems?
What are the fundamental principles of graph technologies that data engineers should be familiar with?
- What are the core modeling principles that they need to know for designing schemas in a graph database?
Beyond databases, what are some of the other components of the data stack that can or should handle graphs natively?
Do you typically use a graph database as the primary or complementary data store?
What are some of the common challenges that you see when bringing graph applications to production?
What have you found to be some of the common points of confusion or error prone aspects of implementing and maintaining graph oriented applications?
When it comes to the specific technologies of different graph databases, what are some of the edge cases/variances in the interfaces or modeling capabilities that they present?
- How does the variation in query languages impact the overall adoption of these technologies?
  - What are your thoughts on the recent standardization of GSQL as an ANSI specification?
What are some of the scaling challenges that exist for graph data engines?
What are the ongoing developments/improvements/trends in graph technology that you are most excited about?
- What are some of the shortcomings in existing technology/ecosystem for graph applications that you would like to see addressed?
What are some of the cases where a graph is the wrong abstraction for a data project?
What are some of the other resources that you recommend for anyone who wants to learn more about the various aspects of graph data?

Contact Info

Denise
- LinkedIn
- @DeniseKGosnell on Twitter
Matthias
- LinkedIn
- @MBroecheler on Twitter

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

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The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

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