Building A Knowledge Graph From Public Data At Enigma With Chris Groskopf - Episode 50
Data Engineering Podcast
Do You Have a Production Environment or a Preproduction Environment?
We all of our work clothes can run first in staging environment and then in production. Nothing gets into production that has run end to end in staging. There will come a point at which we can't keep all those works closed in a single repo. Other problems like that are on the horizon, but we're not quite there yet.
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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- 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 lifecycle of the data that you process from acquisition through to delivery to your customers?
- What are the weak spots in your platform that you are planning to address in upcoming projects?
- If you were to start from scratch today, what would you have done differently?
- What are some of the most interesting or unexpected uses of your product that you have seen?
- What is in store for the future of Enigma?
Contact Info
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Links
- Enigma
- Chicago Tribune
- NPR
- Quartz
- CSVKit
- Agate
- Knowledge Graph
- Taxonomy
- Concourse
- Airflow
- Docker
- S3
- Data Lake
- Parquet
- Spark
- AWS Neptune
- AWS Batch
- Money Laundering
- Jupyter Notebook
- Papermill
- Jupytext
- Cauldron: The Un-Notebook
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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