Episode Details

Summary

Most of the time when you think about a data pipeline or ETL job what comes to mind is a purely mechanistic progression of functions that move data from point A to point B. Sometimes, however, one of those transformations is actually a full-fledged machine learning project in its own right. In this episode Tal Galfsky explains how he and the team at Cherre tackled the problem of messy data for Addresses by building a natural language processing and entity resolution system that is served as an API to the rest of their pipelines. He discusses the myriad ways that addresses are incomplete, poorly formed, and just plain wrong, why it was a big enough pain point to invest in building an industrial strength solution for it, and how it actually works under the hood. After listening to this you’ll look at your data pipelines in a new light and start to wonder how you can bring more advanced strategies into the cleaning and transformation process.

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 Tal Galfsky about how Cherre is bringing order to the messy problem of physical addresses and entity resolution in their data pipelines.

Interview

• Introduction
• How did you get involved in the area of data management? Started as physi