Praveen Murugesan

VP of Engineering, Samsara · 1 appearance on the record.

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engineerexecutiveinvestorLinkedIn ↗sessionize.com/praveen-murugesan ↗

He serves as Vice President of Engineering at Samsara, overseeing IoT and AI-powered products across telematics, safety, and fleet workflows. Previously, he spent several years in engineering leadership at Uber scaling its core data infrastructure and ride experience products.

9statements → 7claims → 0claims resolved → 4.11/5average certainty → 1.33/5average debate potential →

7 not checkable as stated how the 7 claims stand · each chip opens the sources

7 assertions · 1 insight · 1 disclosure · every statement was checked. The predictions and assertions are the 7 claims: statements the public record can support or contradict. 0 are resolved, and 7 name no date, number or outcome precise enough to check. 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 Praveen 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? →

253 words/min while actually speaking · 46.4 um and uh per 1k words

No argument clarity score for Praveen Murugesan: 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: 3,901 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 said9 Episodes1

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

Assertion Not checkable as stated

Uber deployed trip similarity algorithms to fight incentive fraud in China

“Uber used to run this incentive program in China, and, ah, people are trying to game it, so they used to always have, like, similar trips simulated From their, ah, various different devices. So what we did was actually, like, try to find an algorithm where we …”

Praveen Murugesan Sep 30, 2016 ▶ 12:31 The Uber Big Data Story // Praveen Murugesan, Uber (Data Driven NYC / FirstMark)

Assertion Not checkable as stated

Murugesan: Uber practically had no data infrastructure when he joined in 2014

“We practically did not have an infrastructure is what the honest reality is.”

Praveen Murugesan Sep 30, 2016 ▶ 0:20 The Uber Big Data Story // Praveen Murugesan, Uber (Data Driven NYC / FirstMark)

Insight

Schemaless JSON fails as companies scale beyond 50 employees

“It works well, like, if you're, like, a ten-person company, or, like, even to a fifty-person company, when you're, like, scaling to, like, thousands of people, like, you actually need a proper negotiation in between.”

Praveen Murugesan Sep 30, 2016 ▶ 6:56 The Uber Big Data Story // Praveen Murugesan, Uber (Data Driven NYC / FirstMark)

Assertion Not checkable as stated

Murugesan: City operations personnel constitute most of Uber's data consumers

“Most of Uber's data consumers are actually these operations people.”

Praveen Murugesan Sep 30, 2016 ▶ 2:09 The Uber Big Data Story // Praveen Murugesan, Uber (Data Driven NYC / FirstMark)

Assertion Not checkable as stated

Uber consolidated all log and business data into an HDFS data lake

“What we really created was, like, using HDFS, like, a data lake, where we basically copied the whole data sets from, like whatever we get from, like, analytical logs or, like, all our business data sources, too, into HDFS.”

Praveen Murugesan Sep 30, 2016 ▶ 4:28 The Uber Big Data Story // Praveen Murugesan, Uber (Data Driven NYC / FirstMark)

Assertion Not checkable as stated

Streamific powers all data ingestion and aggregation at Uber

“There's a system called Streamific, which powers all of the data ingestion aggregation at this point.”

Praveen Murugesan Sep 30, 2016 ▶ 5:37 The Uber Big Data Story // Praveen Murugesan, Uber (Data Driven NYC / FirstMark)

Assertion Not checkable as stated

Uber transitioned from ETL into Vertica to EL into Hadoop

“We went from an ETL model, where we scraped from, like, the original source, transformed the data and loaded to Vertica, to, like, just an EL model, where we just, like, just copy the data as soon as possible into, like, Hadoop, and all the transformation can …”

Praveen Murugesan Sep 30, 2016 ▶ 6:19 The Uber Big Data Story // Praveen Murugesan, Uber (Data Driven NYC / FirstMark)

Assertion Not checkable as stated

Uber engineers frequently crashed Kafka clusters with unthrottled Spark executor writes

“Kafka was, in general, like, a nice way where people used to pipe the results of, like, their Spark jobs. But often cases, what they do is, like, they hit Kafka hard and bring Kafka down because they're trying to, like, actually send data from, like, hundred e…”

Praveen Murugesan Sep 30, 2016 ▶ 10:21 The Uber Big Data Story // Praveen Murugesan, Uber (Data Driven NYC / FirstMark)

Disclosure

Uber uses cost accounting chargebacks to track data storage costs by unit

“We have something called cost accounting chargebacks is what we call it. So we actually try to, we have a lineage model where we try to figure out who's storing the data. And, ah, we actually kind of can go get a dollar amount of what we are storing.”

Praveen Murugesan Sep 30, 2016 ▶ 18:52 The Uber Big Data Story // Praveen Murugesan, Uber (Data Driven NYC / FirstMark)

Appearances (1)

Episode	Date	Speaking time
The Uber Big Data Story // Praveen Murugesan, Uber (Data Driven NYC / FirstMark)	Sep 30, 2016	17m

Praveen Murugesan

The record, in short

How they sound: speaking style how? →

Appearances (1)

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