Scaling Analysis of Connected Data And Modeling Complex Relationships With The TigerGraph Graph Database - Episode 287

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.

RudderStack Logo

RudderStack provides all your customer data pipelines in one platform. You can collect, transform, and route data across your entire stack with its event streaming, ETL, and reverse ETL pipelines.

RudderStack’s warehouse-first approach means it does not store sensitive information, and it allows you to leverage your existing data warehouse/data lake infrastructure to build a single source of truth for every team.

RudderStack also supports real-time use cases. You can Implement RudderStack SDKs once, then automatically send events to your warehouse and 150+ business tools, and you’ll never have to worry about API changes again.

Visit dataengineeringpodcast.com/rudder to sign up for free today, and snag a free T-Shirt just for being a Data Engineering Podcast listener.

Monte Carlo LogoStruggling with broken pipelines? Stale dashboards? Missing data?

If this resonates with you, you’re not alone. Data engineers struggling with unreliable data need look no further than Monte Carlo, the leading end-to-end Data Observability Platform!

Trusted by the data teams at Fox, JetBlue, and PagerDuty, Monte Carlo solves the costly problem of broken data pipelines. Monte Carlo monitors and alerts for data issues across your data warehouses, data lakes, dbt models, Airflow jobs, and business intelligence tools, reducing time to detection and resolution from weeks to just minutes. Monte Carlo also gives you a holistic picture of data health with automatic, end-to-end lineage from ingestion to the BI layer directly out of the box. Start trusting your data with Monte Carlo today!

Visit http://www.dataengineeringpodcast.com/montecarlo to learn more.

 


Acryl Data LogoThe modern data stack needs a reimagined metadata management platform. Acryl Data’s vision is to bring clarity to your data through its next generation multi-cloud metadata management platform. Founded by the leaders that created projects like LinkedIn DataHub and Airbnb Dataportal, Acryl Data enables delightful search and discovery, data observability, and federated governance across data ecosystems. Signup for the SaaS product today at dataengineeringpodcast.com/acryl


Your data platform needs to be scalable, fault tolerant, and performant, which means that you need the same from your cloud provider. Linode has been powering production systems for over 17 years, and now they’ve launched a fully managed Kubernetes platform. With the combined power of the Kubernetes engine for flexible and scalable deployments, and features like dedicated CPU instances, GPU instances, and object storage you’ve got everything you need to build a bulletproof data pipeline. If you go to dataengineeringpodcast.com/linode today you’ll even get a $100 credit to use on building your own cluster, or object storage, or reliable backups, or… And while you’re there don’t forget to thank them for being a long-time supporter of the Data Engineering Podcast!


Announcements

  • Hello and welcome to the Data Engineering Podcast, the show about modern data management
  • When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!
  • This episode is brought to you by Acryl Data, the company behind DataHub, the leading developer-friendly data catalog for the modern data stack. Open Source DataHub is running in production at several companies like Peloton, Optum, Udemy, Zynga and others. Acryl Data provides DataHub as an easy to consume SaaS product which has been adopted by several companies. Signup for the SaaS product at dataengineeringpodcast.com/acryl
  • RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. Their SDKs make event streaming from any app or website easy, and their state-of-the-art reverse ETL pipelines enable you to send enriched data to any cloud tool. Sign up free… or just get the free t-shirt for being a listener of the Data Engineering Podcast at dataengineeringpodcast.com/rudder.
  • Struggling with broken pipelines? Stale dashboards? Missing data? If this resonates with you, you’re not alone. Data engineers struggling with unreliable data need look no further than Monte Carlo, the leading end-to-end Data Observability Platform! Trusted by the data teams at Fox, JetBlue, and PagerDuty, Monte Carlo solves the costly problem of broken data pipelines. Monte Carlo monitors and alerts for data issues across your data warehouses, data lakes, dbt models, Airflow jobs, and business intelligence tools, reducing time to detection and resolution from weeks to just minutes. Monte Carlo also gives you a holistic picture of data health with automatic, end-to-end lineage from ingestion to the BI layer directly out of the box. Start trusting your data with Monte Carlo today! Visit http://www.dataengineeringpodcast.com/montecarlo to learn more.
  • 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

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.
  • Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
  • If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com) with your story.
  • To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Links

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

Liked it? Take a second to support the Data Engineering Podcast on Patreon!