Ion Stoica
Professor of Computer Science, UC Berkeley. On 2 shows, 3 appearances. The Shows tab opens the full record on each.
academicscientistfounderexecutiveengineerpeople.eecs.berkeley.edu/~istoica ↗Wikipedia ↗
Stoica is a computer science professor at UC Berkeley who co-created Apache Spark, Apache Mesos, and Ray. He also co-founded enterprise infrastructure companies Databricks and Anyscale, as well as AI evaluation platform LMArena.
2shows
3appearances
26statements
10resolved
10supported
0contradicted
100%fully supported
18said about them ↓ Everything Ion Stoica said on any show that made the record, most notable first. Each card names its show and opens the statement there.
Stoica predicts big data infrastructure will eventually unify around one system
“Now, I do think that looking forward you are going to see more and more of this unification. This happens in many other industries and technologies. I do think this will happen in big data because it's so much easier if you have only one system than if you nee…”
Stoica: Top technical experts lack time to perform AI evaluation labeling
“You're getting some people from that area who are willing to do the labeling, but the best people are not willing. Fundamentally, they don't have time.”
Stoica: Apache Spark was created to prove Mesos's value
“So actually, Mesos was the first project we developed. This is what started the stack. And Mesos was by design to support multiple cluster computing framework. We started with Hadoop. And actually, one of the reasons we designed Spark, it's To show that it's m…”
Stoica: Many published AI research algorithms are hard to reproduce
“Many of the algorithms which are published are hard to reproduce.”
Stoica: Apache Spark won the TerraSort benchmark processing data out of memory
“Just October last year, we had this we won this kind of TerraSort benchmark. And in those, in that benchmark, the data, it's not in memory. Right? It's SSDs and so forth.”
Stoica: Apache Spark supports all major data workloads with one engine
“While we spark, You can use only one engine and only one API to support all these workloads.”
Stoica: Over 70% of daily LMArena prompts are completely unique
“And basically measures out how many more, you know, fresh prompts you have in one day compared to what you've seen in the past three months, right? And by a similarity score of something like 70, 75%, you have over 70 of these prompts are fresh.”
Stoica: UC Berkeley's AMPLab generates zero patents and commits to open source
“The other thing about what AMLAB and all these labs have done is that they take a very strong stand about being open source and we generate no patents.”
Stoica: Over 60% of UC Berkeley PhD applicants apply for AI
“When you look at the PhD students who applied to Berkeley, PhD applicants. It turns out that well over 60% are applying for AI.”
Stoica: Computing functionality will migrate bidirectionally between cloud and edge
“Things which are now done in the cloud is going to migrate some of the functionality for on the edge. Also, on, in, on the other side, things which are now done only at the edge, like self-driving cars will migrate, some of the functionality will migrate to th…”
Stoica: Hadoop's HDFS read/write cycle crippled early iterative machine learning
“If you look at the machine learning, it's, fundamentally, it's an iterative algorithm, and every iteration is turned into a Hadoop job. So between the iteration, you write the data and read the data from HDFS, so that's why it's very slow.”
Stoica: Hadoop remains a very great batch processing engine
“Hadoop is still a very great, ah, batch engine.”
Stoica: Spark Streaming and Storm have roughly similar throughput
“They are roughly, you know, in terms of the throughput, they are roughly similar.”
Stoica: Spark Streaming micro-batching is unfit for high-frequency trading latency
“It's very hard to, you know, if you want millisecond latency from the time you, ah, the data entered in the system until you get the result, it's very hard to get. You can get latencies of several hundreds of milliseconds, but milliseconds, very hard. Ah, you,…”
Stoica: Apache Spark has exceeded 500 active open-source contributors
“We exceeded 500 contributors. It is the most active big data project right now, Spark.”
Stoica: Databricks still provides the majority of Apache Spark open-source contributions
“A lot of contributions, still the majority of contributions comes from Databricks.”
Stoica: Users grading answers to their own questions is the gold standard
“This is now from information retrieval field for decades, and it's called gold standard, when people evaluate the answer to their own questions. When an expert evaluates someone else questions, and the answer is called the silver, if I remember correctly.”
Stoica: Facebook's early big data cluster had 80 nodes and three people
“When we started working with Facebook, Facebook, you know, has an entire cluster, big cluster for big data. It was 80 nodes. And their big data team was like three people.”
Stoica: ML models degrade over time as real-world data evolves
“The models you developed on some data set, and for instance, the data or the queries are going to evolve over time. And because the environment or the world around you evolves, what you learned and which is embedded in the model may not be as relevant or as go…”
Stoica: Early Hadoop was limited to batch processing
“So at that point, in big data space we there was Hadoop just started, but of course that was, by, back then it was mostly, you know, batch, computation, so you could do historical analysis, but not much more than that.”
Stoica: Apache Spark was created for iterative machine learning and interactive queries
“And Spark was, ah, you know, we targeted first some workloads which are not covered by Hadoop, and from all this experience I mentioned earlier, we look at iterative, iterative computations to support machine learning, as well as interactive computation, right…”
Stoica: Databricks offers only cloud services, not a custom Spark distribution
“We don't have our own distribution. We provide only the service.”
Stoica: Berkeley's Succinct enables query processing on compressed data
“There is a related project that, ah, Berkeley is called, ah, succinct, ah, which actually go even more than, you know, beyond that. It's, ah it's a project that allows you to, you know, provide allow you to have, you know, query processing on the compressed da…”
Stoica: Silicon Valley proximity keeps UC Berkeley research anchored in industry problems
“Academia, it's allowing you to do more experimentation. It's set for that. And at Berkeley, we are in a privileged position. Of course, being close to the Silicon Valley, we have a lot of feedback. From the industry. So we are very anchored in what are the rea…”
The other half of the tape: Ion Stoica's own voice is left out of every number here. Other people bring the name up 18 times in 7 episodes across the shows. every mention, with the transcript →
Who brings them up most Elena Burger 5Ali Ghodsi 4Michael Franklin 3Haoyuan Li 2Ben Horowitz 2Matt Turck 1Chris Granger 1
Every mention by year
tap a year for its mentions
the a16z Podcast 15
the MAD Podcast 3
2026 5 mentions in 1 episode
2025 5 mentions in 1 episode
2024 1 mention in 1 episode
2021 1 mention in 1 episode
2019 3 mentions in 1 episode
2017 1 mention in 1 episode
2016 2 mentions in 1 episode
One line per show, most statements first. The link opens Ion's full record on that show: the calibration, argument clarity, speaking style and every statement made there.
| Show | Role there | Eps | Statements | Record | |
|---|---|---|---|---|---|
| MADLEDGER | Professor of Computer Science, UC Berkeley | 1 | 14 | 100% 8/8 | full record on the MAD Podcast → |
| a16zLEDGER | Professor of Computer Science, UC Berkeley | 2 | 12 | 100% 2/2 | full record on the a16z Podcast → |