The Hard Problems™️ of Data Observability w/ Kevin Hu of Metaplane
About this episode
As a PhD candidate at MIT, Kevin (and friends) published Sherlock, a data type detection engine (a surprisingly bedeviling problem) for data cleaning + data discovery.
Now as co-founder and CEO of Metaplane, a data observability startup, Kevin applies these same automated data discovery methods to help data teams keep their data healthy.
In this conversation with Tristan & Julia, Kevin wins the coveted award for "most crystal-clear explanations of complex technical concepts through physics analogy."
For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com.
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