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Building A Knowledge Graph From Public Data At Enigma With Chris Groskopf - Episode 50

Building A Knowledge Graph From Public Data At Enigma With Chris Groskopf - Episode 50

Episode 50 Published 7 years, 8 months ago
Description

Summary

There are countless sources of data that are publicly available for use. Unfortunately, combining those sources and making them useful in aggregate is a time consuming and challenging process. The team at Enigma builds a knowledge graph for use in your own data projects. In this episode Chris Groskopf explains the platform they have built to consume large varieties and volumes of public data for constructing a graph for serving to their customers. He discusses the challenges they are facing to scale the platform and engineering processes, as well as the workflow that they have established to enable testing of their ETL jobs. This is a great episode to listen to for ideas on how to organize a data engineering organization.

Preamble

  • Hello and welcome to the Data Engineering Podcast, the show about modern data management
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  • Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch.
  • Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat
  • Your host is Tobias Macey and today I’m interviewing Chris Groskopf about Enigma and how the are using public data sources to build a knowledge graph

Interview

  • Introduction
  • How did you get involved in the area of data management?
  • Can you give a brief overview of what Enigma has built and what the motivation was for starting the company?
    • How do you define the concept of a knowledge graph?


  • What are the processes involved in constructing a knowledge graph?

  • Can you describe the overall architecture of your data platform and the systems that you use for storing and serving your knowledge graph?

  • What are the most challenging or unexpected aspects of building the knowledge graph that you have encountered?

    • How do you manage the software lifecycle for your ETL code?
    • What kinds of unit, integration, or acceptance tests do you run to ensure that you don’t introduce regressions in your processing logic?


  • What are the current challenges that you are facing in building and scaling your data infrastructure?

    • How does the fact that your data sources are primarily public influence your pipeline design and what challenges does it pose?
    • What techniques are you using to manage accuracy and consistency in the data that you ingest?


  • Can you walk through the l
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