Gleb Mezhanskiy

Co-Founder and CEO, Datafold · 1 appearance on the record.

computed by AI from the episodes · how this works → · full disclaimer →

founderexecutiveengineerauthorLinkedIn ↗datafold.com ↗

Gleb Mezhanskiy is the CEO and co-founder of Datafold, an automated data quality platform. Before founding Datafold, he built and scaled large data platforms as an early data engineer at Autodesk, Lyft, and Phantom Auto.

4statements → 1claims → 0claims resolved → 4/5average certainty → 1.75/5average debate potential →

1 not yet assessed how the 1 claim stands · each chip opens the sources

1 assertion · 2 insights · 1 disclosure · every statement was checked. The predictions and assertion are the 1 claim: statements the public record can support or contradict. 0 are resolved, 1 is not yet assessed. Everything else (opinions, insights, what ifs, disclosures) can never be settled by the record, so it carries no assessment.

The record, in short

What the tape says about how Gleb argues and how the claims held up. Everything they said, and everything said about them, is in the tabs below.

How they sound: speaking style how? →

247 words/min while actually speaking · 17.3 um and uh per 1k words

No argument clarity score for Gleb Mezhanskiy: no usable question→answer exchanges on raw tape (a fair score needs 8+). We do not score a sample that small. Roundtable and news formats yield far fewer direct exchanges than interviews.

Measured by listening to the audio itself: 3,236 words across 1 episode of raw-level tape, transcribed verbatim with every um and uh kept, each one attributed only where the alignment onto our timed stream is unambiguous. These are measurements of speaking style. We do not rank them: across this corpus, fluency and argument quality are nearly uncorrelated (ρ≈0.2), and smooth talking does not signal clear thinking. How it's measured →

What they said4 Episodes1

Everything Gleb Mezhanskiy said on the MAD Podcast that made the record, most notable first. Filter by type, assessment or year in the ledger →

Insight

Gleb Mezhanskiy: Data monitoring scales better than manual data testing

“Data testing is basically us writing unit tests for data are not scalable, whereas data monitoring is scalable because we can just automatically track data in the warehouse and automatically provision machine learning to track data for anomalies.”

Gleb Mezhanskiy Sep 12, 2022 ▶ 5:45 A Novel Approach to Data Quality for the Modern Data Stack | Datafold’s Gleb Mezhanskiy

Insight

Gleb Mezhanskiy: Data testing gives better signal-to-noise than data monitoring

“The signal to noise in data testing typically tends to be better because we define exactly what is wrong, what is right, versus in monitoring, it's Quite noisy, because we just, it just tells us when data doesn't conform to the historical properties, which doe…”

Gleb Mezhanskiy Sep 12, 2022 ▶ 5:59 A Novel Approach to Data Quality for the Modern Data Stack | Datafold’s Gleb Mezhanskiy

Assertion Not publicly verifiable

Mezhanskiy: Datafold's data-diff benchmarks over 1B rows in 5 minutes

“We run some benchmarks, and you can run this on like a twenty-five million row data set in less than 10 seconds, An over one billion row dataset in about five minutes.”

Gleb Mezhanskiy Sep 12, 2022 ▶ 16:37 A Novel Approach to Data Quality for the Modern Data Stack | Datafold’s Gleb Mezhanskiy

Disclosure

Gleb Mezhanskiy: A three-line SQL hotfix once crashed Lyft's data platform

“In my years of data engineer at Lyft, I was unlucky to break down, well, actually blow up the entire data platform by making a three line SQL code hotfix that filtered a little bit more rights than I anticipated.”

Gleb Mezhanskiy Sep 12, 2022 ▶ 2:25 A Novel Approach to Data Quality for the Modern Data Stack | Datafold’s Gleb Mezhanskiy

Appearances (1)

Episode	Date	Speaking time
A Novel Approach to Data Quality for the Modern Data Stack \| Datafold’s Gleb Mezhanskiy	Sep 12, 2022	17m

Gleb Mezhanskiy

The record, in short

How they sound: speaking style how? →

Appearances (1)

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