Clean Up Your Data Using Scalable Entity Resolution And Data Mastering With Zingg
Data Engineering Podcast
Shownotes Transcript
Summary
Despite the best efforts of data engineers, data is as messy as the real world. Entity resolution and fuzzy matching are powerful utilities for cleaning up data from disconnected sources, but it has typically required custom development and training machine learning models. Sonal Goyal created and open-sourced Zingg as a generalized tool for data mastering and entity resolution to reduce the effort involved in adopting those practices. In this episode she shares the story behind the project, the details of how it is implemented, and how you can use it for your own data projects.
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 Sonal Goyal about Zingg, an open source entity resolution framework for data engineers
Interview
Introduction
How did you get involved in the area of data management?
Can you describe what Zingg is and the story behind it?
Who is the target audience for Zingg?
How has that informed your efforts in the development and release of the project?
What are the use cases where entity resolution is helpful or necessary in a data engineering context?
What are the range of options that are available for teams to implement entity/identity resolution in their data?
What was your motivation for creating an open source solution for this use case?
Why do you think there has not been a compelling open source and generalized solution previously?
Can you describe how Zingg is implemented?
How have the design and goals shifted since you started working on the project?
What does the installation and integration process look like for Zingg?
Once you have Zingg configured, what is the workflow for a data engineer or analyst?
What are the extension/customization options for someone using Zingg in their environment?
What are the most interesting, innovative, or unexpected ways that you have seen Zingg used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on Zingg?
When is Zingg the wrong choice?
What do you have planned for the future of Zingg?
Contact Info
@sonalgoyal) on Twitter
sonalgoyal) on GitHub
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 shows. Podcast.init) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast) helps you go from idea to production with machine learning.
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Links
The intro and outro music is from The Hug) by The Freak Fandango Orchestra) / CC BY-SA)