Summary

Many of the events, ideas, and objects that we try to represent through data have a high degree of connectivity in the real world. These connections are best represented and analyzed as graphs to provide efficient and accurate analysis of their relationships. TigerGraph is a leading database that offers a highly scalable and performant native graph engine for powering graph analytics and machine learning. In this episode Jon Herke shares how TigerGraph customers are taking advantage of those capabilities to achieve meaningful discoveries in their fields, the utilities that it provides for modeling and managing your connected data, and some of his own experiences working with the platform before joining the company.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management
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Your host is Tobias Macey and today I’m interviewing Jon Herke about TigerGraph, a distributed native graph database

Interview

Introduction
How did you get involved in the area of data management?
Can you describe what TigerGraph is and the story behind it?
What are some of the core use cases that you are focused on supporting?
How has TigerGraph changed over the past 4 years since I spoke with Todd Blaschka at the Open Data Science Conference?
How has the ecosystem of graph databases changed in usage and design in recent years?
What are some of the persistent areas of confusion or misinformation that you encounter when explaining graph databases and TigerGraph to potential users?
The tagline on your website says that TigerGraph is "The Only Scalable Graph Database for the Enterprise". Can you unpack that claim and explain what is necessary for a graph database to be suitable for enterprise use?
What are some of the typical application and system architectures that you typically see for end-users of TigerGraph? (e.g. polyglot persistence, etc.)
What are the cases where TigerGraph should be the system of record as opposed to an optimization option for addressing highly connected data?
What are the data modeling considerations that end-users should be thinking of when planning their storage structures in TigerGraph?
What are the most interesting, innovative, or unexpected ways that you have seen TigerGraph used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on TigerGraph?
When is TigerGraph the wrong choice?
What do you have planned for the future of TigerGraph?

Contact Info

LinkedIn
@jonherke on Twitter

Parting Question

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

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used.
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Links

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

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