The Death of Big Data and Why It’s Time To Think Small | Jordan Tigani, CEO, MotherDuck
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What is the main topic discussed in this episode?
Welcome back to the Mad Podcast.
What is “Small Data” and why does Jordan Tigani say Big Data is dead?
Today my guest is Jordan Tigani, CEO of cloud analytics company MotherDoc.
How does MotherDuck’s marketing strategy differentiate it from traditional Big Data vendors?
MotherDuck and the DuckDB open source project have become very buzzy in the world of data infrastructure with the promise of delivering fast, efficient analytics without the complexity of traditional big data solutions.
Why does processing Small Data with a Big Data stack still make sense for some workloads?
We talked about why big data is dead and the rise of small data.
What is DuckDB and how does it enable fast analytical queries?
If you have smaller amounts of data, you can move faster, less expensively, because the architecture is simpler. We can focus more on building better experiences.
How did the founding story of MotherDuck evolve from the creation of DuckDB?
The
mother duck product and its focus on
speed. We can do queries in sort of single digit milliseconds. And BigQuery, we were very, very happy when we got the overhead down to like four hundred milliseconds.
Why are MotherDuck and DuckDB able to run queries in single‑digit milliseconds?
And Jordan's unlikely entrepreneurial journey.
I always figured that there's people out there that are like gonna start companies and then there's like kind of normal people. And I was one of the the normal people
please enjoy this great conversation with Jordan. Uh it feels like the absolutely unescapable, unavoidable way to start this conversation is to talk about small data.
How does shifting from Big Data to Small Data simplify modern data‑stack architectures?
Just when we thought we had finally made it in the world of big data, uh, you came up with a very well um written and very noticed uh blog post in early 2023 uh called Big Data Is Dead. Uh and like you built a whole thing uh around ran this and uh just last week in San Francisco you had the you ran the the small data conference. So small data, what is it all about?
Um, so I'm very glad you said that it was unavoidable. Like that, I mean, we we did we did a lot to try to get the message out and get people get people excited. And um, you know, it feels like it's a little bit sort of counter counter the prevailing narrative that uh, you know, everything for 15 years has been about big data this, big data that, how big's your data? Uh how much can you scale? And um kind of the tipping point for me. was when um I saw the you know the sort of Databricks versus Snowflake kind of they had this benchmarking war and um and everybody's focused on like the the the you know the war between Databricks and Snowflake. And to me the biggest thing that I noticed was like, well they're looking at
the database benchmarket, the the database this the the query sizes they were using was 100 terabytes. And I remembered back from my time at BigQuery, um, you know, we had some of the largest customers in the world. We had Walmart, Home Depot, Equifax, HSBC, you know, like And and nobody was using anything, you know, running queries anywhere near that. Because it would have actually fallen over at the time. And um so I I I knew that and people like were um, you know, were kind of not even really pushing up against against limits. And so I I thought like, wow, if if this is sort of the state of the art that people are focusing on this size of data that nobody has, um, there's gotta be an opportunity actually to sort.
of like look at the smaller, you know, smaller data sizes. And I and I kind of was remembering back um to when I was doing a bunch of analysis, uh also when I was at BigQuery on the you know the query sizes that people were using and most people actually were use you know had small data. The amount of data they were actually using was even smaller than that. And um and Uh and so kind of it was like, you know, hey, I I bet if you were gonna design a system These days from scratch, like you do it differently. You know, after Google came out with, you know, MapReduce and and GFS and Big Table, kind of everybody's
All of which was in like two thousand six, right? So we're going on twenty years.
Everybody's brain just sort of like broke and they're like, Wow, in order to build systems that can handle the data sizes that we're seeing, you kinda you have to just dramatically change how you're building them. You have to s you have to run on lots of machines, lots of cheap, inexpensive machines versus you know these giant hyper-expensive machines. And to be fair, that was a problem at the time.
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Chapters
8 chapters
1
What is the main topic discussed in this episode?
0:00–0:02
2
What is “Small Data” and why does Jordan Tigani say Big Data is dead?
0:02–0:08
3
How does MotherDuck’s marketing strategy differentiate it from traditional Big Data vendors?
0:08–0:20
4
Why does processing Small Data with a Big Data stack still make sense for some workloads?
0:20–0:24
5
What is DuckDB and how does it enable fast analytical queries?
0:24–0:32
6
How did the founding story of MotherDuck evolve from the creation of DuckDB?
0:32–0:43
7
Why are MotherDuck and DuckDB able to run queries in single‑digit milliseconds?
0:43–1:04
8
How does shifting from Big Data to Small Data simplify modern data‑stack architectures?
1:04–59:00
Speakers
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