From Rows to People - by Tristan Handy - The Analytics Engineering Roundup

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2021-09-27 07:00:07

Hope you’re having an excellent weekend. Make sure to subscribe to the podcast, as in the most recent episode Julia and I talk about developing junior analytics talent with Brittany Bennett of the Sunrise Movement. Brittany has thought more about growing the humans on her team than potentially any data leader I’ve met.

Well, who are you? (Who are you? Who, who, who, who?) I really want to know (Who are you? Who, who, who, who?) Tell me who are you? (Who are you? Who, who, who, who?) Because I really want to know (Who are you? Who, who, who, who?) - The Who, Who Are You?

If you have never had to hand-code basic entity resolution in a dbt model, well…I cannot help but be envious. Entity resolution—one of the most dry subjects imaginable (you’re welcome)—is one of those problems that seems, on its surface, to not be that hard. And then you just keep digging and digging and never hit the bottom.

Entity Resolution is a technique to identify data records in a single data source or across multiple data sources that refer to the same real-world entity and to link the records together. In Entity Resolution, the strings that are nearly identical, but maybe not exactly the same, are matched without having a unique identifier.