Fivetran CEO George Fraser

This transcript was automatically generated and edited for clarity.

Sabrina: Hi everybody, I’m Sabrina Wu, and I’m an investor at Madrona. I’m excited to be here today with Fivetran CEO and Co-founder of George Fraser. It should be no surprise that Tran was selected as a top 40 Intelligent Application in 2021. Intelligent applications leverage artificial intelligence and machine learning to continuously become better at solving business problems. However, the data ingestion, preparation, and management challenges that enable intelligent applications are extremely difficult to solve, which is why intelligent applications require enabling layers, such as Fivetran. Fivetran has built fully automated data connectors that enable businesses to extract, load, and transform data from different cloud storage and database sources into one central data storage system. I’m excited to dive into more of this with George today.

George, thanks so much for being here with us.

George: Very nice to be with you. That was a great overview. And applications, of course, our original bread and butter — things like Salesforce, NetSuite, stuff like that.

Sabrina: That’s a great transition. Let’s go back to the beginning —how did Fivetran go from being a data analysis tool to becoming the leading provider of automated data integration? Was there some light-bulb moment for you guys that made you want to pivot the business?

George: Well, it took about two years. Basically, we figured out one thing in those two years, which is that we would find product-market fit by cutting out everything except the data integration. So yes, we built a vertically integrated tool that had connectors as part of it, that stored data behind the scenes and Redshift, and that provided you a user interface to interact with that data. And, it was, it was not great. It was a vertically integrated BI tool built by three guys, and we worked hard at it, but it had a long way to go. What happened was we started to get this request from people we were talking to, which was, “Hey, I’ve got my own Redshift cluster that I’ve just set up,” and this was 2014, right? So, Redshift was this phenomenon in data warehousing at the time. It was the first data warehouse that was fast and cheap. It was available in the AWS console. And at the time, it was the fastest-growing product in AWS’s history. So, we were talking to all these people, and more and more started to say, “I’ve got my own Redshift cluster that I’ve just set up. It’s empty, and the problem I have is just getting data into it. And I like the sound of these connectors that you’ve built as part of this system. Can I just get those into my own Redshift cluster? And then I have my own plan of what I wanna do with the data.” And eventually, we said yes to one of them, and then another, and then another, and that became the company.

Sabrina: And was it obvious at the time that these automated connectors would be so valuable to customers? And obviously, the database world has exploded since then. I’m curious if the problem has been much bigger than you had initially anticipated?

George: It has indeed. It’s turned out to be more valuable and more widely popular than I think anyone anticipated. It’s funny because this is not a new problem. Data integration — taking data from a bunch of systems of record, whether they be databases or applications, and putting it into a central data warehouse — has been around since the 1980s, so it’s not a new problem. There have been tools for doing this for years and years. But the tools were not very good at the time — in my humble opinion. They didn’t really solve the problem. They were like a pile of building blocks upon which you would build your own integrations, and you were responsible for the correctness and reliability of those connections. If it broke, that was your fault. You had to figure out why. So, most people were just writing code to do data integration. I had actually done a lot of that at my previous job in a very different context in biotech. But this is a problem that had been around for a long time. It was everywhere. You talk to 10 software engineers, five of them have written some code to do data integration inside their company at some point. There had been companies founded trying to solve this problem. None of them had ever really taken off. They were a fraction of the size of the data warehousing companies, even though logically, both always have to exist in every account. So why was this such small potatoes? Nobody had ever really found a good solution to this problem.

And we completely ignored the way it had been done before. There was a lot of conventional wisdom about the right way to do data integration, and we just totally ignored it. And we said, Okay, what seems like the right way to us? We should just replicate everything. You know, the data starts in the place that it lives. Replicate everything into a normalized schema. The destination is a database, so people can do whatever they want with it from there. As long as you get it all there in a correct replica, you’ve solved a big chunk of the problem. And the nice thing about that is it’s like a bounded problem. You don’t get sucked into all the workflows that the user intends to do on the other side. What is your definition of revenue? You don’t get pulled into that. You’re like, “Look, I’m going to replicate the amount field from the opportunity table in Salesforce, and I’m going to do that correctly — that’s my job. How that maps to your revenue recognition roles, that’s for you to figure out.” So, there was a nice, clear separation of concerns between what our job was and then the problems that remained to be solved by the user. And the part of the problem that we were taking on was a big chunk of the problem. It’s not the whole problem. And that was kind of the conventional wisdom we were walking away from, that you had to tackle those downstream elements. We said, “No, we don’t. We’re going to solve the replication portion of this, which is actually a pretty big chunk of the problem. That’s where we’re going to focus our efforts — correct replication. And the very first connector was for Salesforce, and then we did Zendesk and Stripe, and then we started doing databases and a bunch of other things. But we always took that philosophy like Fivetran is fundamentally a replication tool, and you are going to work out the semantics of that data in the destination after it arrives.

Sabrina: I think one of the reasons customers really love Fivetran is because the product itself is very simple to use, but in reality, you are solving a very complicated and difficult problem — something that is very cumbersome and tedious … an engineer spent a lot of time building this. You started to talk about it, but I’d love to understand Fivetran’s approach to ELT. So, extract, load, transform, which shifts, as you were saying, the transform step to the end of the process. Can you talk a bit about why Fivetran created this new approach and the evolution of ETL — extract, transform, load to ELT — extract, load, transform?

George: It’s interesting. You know, ELT is a little bit of a lie. It’s a lie that reveals an essential truth. But the reality is that Fivetran does a ton of transformation. So, it’s really ETLT. The way the data comes out of the source, people always imagine what the source gives us is like a nice little list of inserts, updates and deletes, and then we just take it over to the destination and load it in. If it worked like that, we’d have lots of good competitors by now, and we don’t. So, the reality is the sources are sort of crazy in what they give you, so we have to develop these extremely convoluted rules for reconstructing the set of what has changed from the source. And it’s different for every source. But, if you make your goal to produce a correct replica, then that problem is the same for every user. So it’s very complicated. There’s a ton of complicated transformation we’re doing under the hood, but it’s always the same, whether it’s your Stripe account or someone else’s Stripe account or whatever the case may be. Stripe is actually not as hard as some of the other ones. That’s not a great example. Stripe gives you the data in a pretty good format, but if it’s your HANA database versus someone else’s HANA database, the rules of stitching together changes are going to be the same. They’re very complicated, but they’re always the same. And so, we do that level of transformation, but then we abdicate this other job, of like, how do you map all this data onto the concepts of your business. There’s another layer of transformation that is different for every customer. And this is the problem we very intentionally chose not to tackle. We said, “You’ve got to solve that after we deliver the data, and there are multiple ways to solve that problem.” But by making this choice, it sort of gives us a problem to solve and the customer a problem to solve. And it’s a very nice, clean handoff. And so you get that very simple user experience. Fivetran is doing very complicated things under the hood, but you don’t need to be involved in that. You don’t need to know how that works because it’s fundamentally the same for every user, so we can just automate it behind the scenes.

Sabrina: That’s a good point. And I think that what you’re talking about here is also Fivetran’s big investments in change data capture, so this whole process of CDC as well as replicating and allowing customers to move really large amounts of volume with very low impact. I know Fivetran recently also acquired HVR to further strengthen this. And so, I’d love to understand why data replication is increasingly important for customers.

George: Well, I don’t know if data replication is increasingly important. It’s just always been very important. Maybe for smaller companies. You know, one thing we have noticed is that historically data warehouses were something that were only really created by the largest companies. And we have an astonishing number of like 50-person and under companies building data warehouses, which historically that was not really a thing. But the problem has become so much easier at every layer of the stack that it’s become feasible for much smaller companies to pull this off. Then they can realize the benefits of knowing what’s going on in your business, which is the primary benefit of a good data warehouse.

Sabrina: When you think about CDC as a whole — why is this problem so difficult to solve?

George: It’s just a maze of incidental complexity. There’s nothing really fundamentally hard about it in the way that building a globally distributed transactional database is just like a very fundamentally hard problem, and people write Ph.D. thesis about it. Nobody writes a Ph.D. thesis about data integration. It’s just a huge forest of incidental complexity, and the way we’ve been able to tackle it is with a lot of grit, a lot of hard work, and a lot of time. If you look at the history of Fivetran, we talked about the original pivot to just data integration in 2015, but then for the rest of 2015 and a lot of 2016, we didn’t grow that fast because it still didn’t work that well. Part of it just took time to figure out all these little rules that create that easy user experience where you just push the button, and your data appears and keeps itself up to date, and the details of how that happened are unknown to you. So, a lot of it has just been time. As the years have gone by, we have studied ourselves, and we have started to learn how to solve some of these problems more systematically and, in fact, automate the process of developing connectors internally at Fivetran. And you will notice if you look at our release notes that the rate of new connectors being released in the last couple of quarters has increased dramatically. Our CTO Meel Velliste and a small group of engineers spent a bunch of time last year really studying basically all of these quirks that we had observed in every connector we had ever built. And they sort of mapped it out and determined that data sources do a lot of crazy things, but there is a finite number of crazy things. You can kind of catalog them all — all the weird stuff that we’ve encountered over the years. And then they developed a new way of building connectors, like a configuration-driven connector where you just have to describe, within this vocabulary, how any particular data source works. And you don’t have to implement all the procedural rules to correctly deal with all of those quirks. And so that’s a pretty cool thing — it took years to figure that out and to do that right. But you’re seeing the benefits of that now with the acceleration of how many new connectors we’re able to build each month.

And we do hope to get to the point in the next couple of years where we have thousands of connectors. Cause that’s what we ultimately need to have. There are so many data sources out there. And it is extremely valuable to customers, especially larger customers, if Fivetran can cover all their data sources — if they can say, “I can adopt Fivetran, and then my data integration problem is solved.” Whatever the source is, Fivetran’s going to have a connector to it.

Sabrina: That’s a really good point. And I’m curious — you also have decided not to open source, to my understanding, any of your connectors.  

George: We never seriously considered open sourcing our connectors. The way we looked at it is our users are not software engineers. They’re analysts and data engineers. and most of them aren’t data engineers, by the way. So they don’t really have the ability or the desire to contribute to these connectors. Open source works really well when your users are software engineers who have the ability and the interest in contributing back to things like Postgres, for example, or Linux. For us, that’s mostly not the case. And the other problem is — when people build connectors for themselves, they tend to take shortcuts that work for their particular situation but won’t work for others. They rely on behaviors of how they use the underlying system. Like, oh, you know, when I update rows in this table, I always update this column named “updated at,” so I can use that in order to get changes from that table. Well, that might be true for you. But it’s not true for others. And so that trick is not going to work. And so, for all these reasons, we didn’t really see a community of contributors being a good way to get high-quality connectors. And I think that hypothesis has mostly been born out over the last few years. Fivetran continues to have a big advantage in terms of the quality of our connectors, by which I mean, are they working? And is the data in the destination actually a correct copy of the source? That is really the hardest part.

Sabrina: Got it. In my mind, Fivetran targets a lot of people and adds a lot of value to multiple people in the organization. So it could be the data engineer, the data analyst, the business analyst, right? And so I’m just curious, how are you building the product with these people in mind? You touched on it a little bit there, but I’m just curious if there are certain use cases where it’s better suited for collecting and analyzing data. How do you think about this from a data analysis versus ML workloads?  

George: You know, it’s funny, uh, the answer to that is it really doesn’t actually matter to us. This is something that was al always very frustrating to VCs and often to salespeople — this is not really a persona-driven problem or product. We deliver data. The data needs to be correct. It needs to be up to date. And you can do a lot of different things with that data. And our users do a lot of different things with that data, and oftentimes, the proximate user of Fivetran, like the person who we’re talking to at the company, they don’t even know what all people are doing with this data. There’s this great mantra, “Data attracts workloads.” You know, once you have a good central store of all your company’s data, people will just come up with all kinds of stuff to do with it.  

Sabrina: That’s funny. I think people are always kind of asking who’s the right persona. Who’s the right user, right? But um, in reality, when you’re thinking about some of the plumbing work that goes in that Fivetran is working on, it really is impactful to the whole organization. Now I’d love to kind of shift gears a little bit and get your thoughts on the modern data stack. In today’s world, customers are really patching together a lot of different best-in-bread tools and solutions to host, store, transform, and eventually analyze and understand the data. That’s the end goal that customers have. And so, as the number of tools has proliferated, there’s been more complexity than ever before. And I’m just curious how does Fivetran continue to differentiate in a very competitive and complicated data stack?

George: Yeah. You know, people make these like market maps, and they put all these logos on them. By the way, if you look closely at those market maps, the same logos will appear multiple times in different categories. So it makes it look a lot more complicated than it is. And the other thing is, you don’t have to use all these tools. I mean, the first few Fivetran customers, most of them, used Redshift, Fivetran, and Looker. Which was at that time a relatively new BI tool, but it had this key strength, which was this modeling layer called LookML, which allowed you to do that post-Fivetran transformation of data that we required you to do because we had abdicated that job. And LookML has largely now been replaced in that role by dbt, which just does that. But the point I’m making is that stack is still available. You can still buy that today. It still works just as well as it ever did. You don’t actually have to adopt all of these other things. I don’t think it really has gotten more complicated. I actually think it’s gotten quite a bit simpler. You know, if you look at people who are running Fivetran, a good cloud data warehouse, like Snowflake, Big Query, Redshift, Databricks, and dbt to manage their transformations, life is pretty good for them. A small number of people can manage a lot of data and a lot of complexity with that set of tools, and it’s a pretty good stack to adopt.

Sabrina: Yeah, I tend to agree with that. Do you think that there’s going to be more consolidation here in the years to come?  

George: There’s always consolidation happening. So, the short answer is yes. And how things consolidate. What are the key strategic workflows that they consolidate around is always a big question. I think we’ve seen some of that happen. As you mentioned earlier, Fivetran acquired HVR. And the reason for that was very simple. HVR was really good at replicating the biggest, most difficult enterprise databases at low latency and the highest volumes. And Fivetran was really good at everything else. And so, it was a perfect marriage. And for users, now you have one vendor that can do both. And we’ll probably see other things like that. It’s something we do think about a decent amount at Fivetran — how much are acquisitions going to be a part of our future … probably a medium amount. So yeah, I do think there will be consolidation. And I do think that Fivetran is going to be one of those key poles that consolidation happens around. Because we’re at the top of the funnel, right? We’re getting the data from everywhere. We control the rules of how the schemas work and how the updates happen, and when things happen. And for that reason, there are a lot of other secondary workflows that make sense to pull into that ecosystem.

Sabrina: At what point would you consider acquiring a company versus maybe just developing stronger partnerships with those companies? Integrating more into the workflow?  

George: We’ve only done it a couple of times. You know, there was HVR, and there was a much smaller company called Teleport, which we acquired for an algorithm also for replicating databases and very different circumstances. We are mostly a partnership-driven company. Always have been. Our second customer was a partner referral, so most of the time, the answer is partnerships. And you see that right now with our approach to metadata. Fivetran has all this metadata about the data that’s sitting in your data warehouse: where did it come from? When was it last updated? And we are working to expose all that data. The only times when acquisitions have come into the picture is when, you know, it’s something that’s in that core focus that we felt we were not good at and needed to get better at, and it was going take a long time to do it ourselves. But, as I said earlier, we’ve only done that twice. So how that philosophy will evolve in the future, I can’t say.

Sabrina: Well, it might be a good time to be on the offensive and get some acquisitions under your guys’ belt, given a lot of what’s going on in the macro environment today. But I think that your point about partnerships is really, really important. I think Fivetran is one of the companies that has partnerships with almost all of the key players in the modern data stack ecosystem, Snowflake, as you mentioned, dbt, Collibra, Hightouch, and some of the governance players now with the release of Metadata API. And so, I’m just curious, you know, how have you been able to maintain these partnerships? And it seems very core to the Fivetran strategy, and I would just love to touch a little bit more upon this partnership strategy that you guys have built over the years.

George: Yeah, it’s, well, we have a partnerships team that works very hard. Part of it is just intrinsic to what we do. We do something that all the partners need. None of these tools do anything without data in them, and that’s what Fivetran does — it feeds the data. There is an element of like, we’ve become partner driven just because we do something that all these partners really care about and need us to do. We’ve always tried to just be very straightforward in our strategy and in the way we communicate that to partners.

You know, like I said, the core of Fivetran is connectors and always will be— we’re fundamentally a data movement company, and that’s not going to change. So, we’re very predictable in terms of what we’re going to do next. And then in the details of that, we’re very transparent with our partners about like, “Hey, this is what’s happening next quarter. This is what we think is going to happen next year,” and I think that helps.

Sabrina: There have been some of these newer concepts thrown around. As I’m sure you know, reverse ETL emerged a couple of years ago, and so initially, data warehouses were introduced to essentially eliminate the data silos, right? But for many companies, data warehouses have now become a data silo, you could argue. And so, there’s this concept in the modern data stack known as reverse ETL, which aims to remove the barriers between the data warehouse and all the end applications such that teams can operationalize data into the systems themselves. What is your thinking around reverse ETL?

George: Reverse ETL is such a funny name — it’s a good name because you immediately know what it is. But then, after you think about it for 15 seconds, you’re like, wait, this is also ETL. It’s not reversed in any way. It was originally a Fivetran feature request. Our very first customer asked us to do that within weeks of going to production. And they were not the only ones. And the problem was forwards ETL is hard enough. We had to just focus on that. And we talked very openly about it with other players in the ecosystem and with friends, including the founders of Census, actually. I’m not taking credit or anything, but we did tell them about how we kept getting this request before they started Census, and that was probably one of many pieces of evidence that contributed to their decision to attack that problem. So, it’s a need that is out there. It hasn’t gotten as big as we have just yet, and time will tell how big the demand for that actually is. I think that’s the key question, is this idea of using the data warehouse as a data bus that you can then send data onward to other places. It’s definitely a thing people are doing. Is that something everyone is going to do in a couple of years or is that going to be something some people do? Time will tell.

Sabrina: Yeah. Census and Hightouch, right? Those are the two going after it in the reverse ETL world. There are also some newer approaches around query federation that suggest that engineers and developers may no longer need ETL altogether because you can query data regardless of where it lives. So, you’re removing this step of ETL altogether. Do you think ETL will continue in light of the federation trend?  

George: What trend? Query federation has been around for decades. And it has been a stupid idea the entire time. Query federation makes for an incredible demo. You have live access to data instantly. You don’t have to wait for historical backfills or anything. And as soon as you go into production, it will fall over because you will discover that the data sources are simply too slow to give you data fast enough to support realistic queries. There are these little optimizations you can do, like predicate push down, that will sometimes speed it up. But as soon as you go to production, you’re going to discover you have a lot of queries that are not subject to those optimizations, and you have to move the data. There is an exception, which is if you’re reading data from object storage like S3, that actually does have enough bandwidth to make query federation work. But other than that, it’s hopeless. You can’t read the data fast enough to support a real data warehouse workload using query federation. It’s like a beautiful dream. And I will say that in some ways, you know what Fivetran did, where we said we’re going to move the data, but we’re going to treat this as a replication problem. In many ways, we are creating the same user experience as query federation. You know, you look at your data warehouse, and you just see exactly the same schema as exists in all the data sources, except the way it is actually implemented is using data movement because it’s impossible to do anything else. So those are my thoughts on query federation.

Sabrina: I love the hot takes. It’s always great to hear, you know, differing perspectives around what’s “in” and what’s not, and I’m curious if there’s any other, you know, approaches that keep you up at night or anything else that has been a concern — something that you are just thinking through as Fivetran. Like, “Hey, there’s all these different approaches now to moving the data to being able to analyze that data.”

George: Yeah, I think that there are two limitations of, let’s call it, the Fivetran-centered data stack. And they’re really limitations of the destinations of the data warehouses we deliver to, and opportunities for those data warehouses to fix these limitations. And they’re working on it. One is latency. It’s pretty hard to do any better than 15-minute latency the way things currently work. And there is a set of use cases where that’s not good enough, but it is really hard right now to do better. So the world needs a low latency data warehouse, and I think that will happen. I think the great data warehouses that exist today are going to make progress on this. They are making progress on this. And we may see another player emerge where the primary value proposition is they just have much lower latency.  Then the other problem is, you know, the usage of the data warehouse as a data bus where you centralize all this data in your data warehouse, but then not everyone wants to run SQL queries. Some people want to just pull the data out and send it on to some other system that does something with it, and it’s just very costly to do that by running SQL queries against a data warehouse. And so, we’re seeing a couple of different ideas emerge about how to solve that problem. So, some people try to adopt something like Kafka as a central data bus to solve this. Now you have a whole different set of problems. And I think that’s unworkable for very complicated reasons that have to do with the way the data comes out of the sources. People don’t realize the sources produce very unclean data. And if you’re not sending the data to a relational database that supports updates and things like that, the data you’re going to be looking at is going to be very ugly.

That’s one vector people are pushing on to try to attack this problem. Another vector is like data lakes. The problem with data lakes is that the latency is even worse. Big Query has done something interesting here. They have this thing called the Big Query Storage API, where you can, basically cheaply, directly read data out of the underlying storage layer of Big Query. So, if you want to do something that isn’t a SQL query with the data, you can use Big Query as a data bus. But no one is using this. That’s kind of a big problem that I’m thinking a lot about right now — how do we attack those twin problems of latency, and how do I support workflows other than SQL queries at reasonable cost and performance none of the solutions that exist right now look great.

Sabrina: Yeah, I like how you frame these two problems. I think they’re definitely key pain points that we’re also seeing, and I think as analytics are moving more towards the edge, especially for use cases around things like streaming and data apps, and as existing workloads grow, it’ll be interesting to really see how some of these new technologies play out in the fullness of time.

So just to wrap up here a little bit. It’s really impressive how fast Fivetran has grown over the last two years, and as you said before, it hasn’t always been an easy journey. It took two years of solving a different problem before pivoting, and then it took some time to get some of those first customers. I’d love it if you could share some of your key learnings for founders.  

George: It’s funny, once it was passed a hundred people, it looked the same the whole time. It helped that our growth was extremely steady over the last, you know, five years. So, if you’re growing, you know, 100% a year, that’s hard. But it’s really hard if you grow 200% one year and then 20% the next. It helps if things are steady. Again, that’s something that is as much a property of the market as your company, so you don’t exactly have control over that. So, it’s not maybe very actionable advice. But that has definitely helped. Another thing that has helped is a large percentage of the leaders of the company are people who have been here for many years. We’ve had a very stable leadership team. I think a lot of startups will replace their key leaders of all the key functions every time the company gets twice as big. And if you have to do that, you have to do that. But, through a mix of luck, of, I guess, hiring the right people early, and of really giving people a chance to learn and improve themselves, we’ve been able to maintain a more stable leadership team than I think most startups who have gone through such rapid growth. And that has some big benefits in terms of like controlling the amount of drama as you grow, you know, having these people who have been here for a long time, who have seen it grow up, they can manage certain categories of problems that are harder when it’s a revolving door.

Sabrina: Okay, so I wanted to wrap today with a round of lightning questions. So we asked this to all of our guests on the intelligent applications podcast series, and so — aside from your own, what startup company are you most excited about in the intelligent application space and why?

George: Probably Materialize. I should disclose that I am a very small angel investor in Materialize. The reason that they are so exciting is that they have started with the hardest problem — the hardest unsolved problem, let’s say, in database management systems, which is materialized views. And then worked outward from there. Whereas everyone else starts with all the things that you already know how to do, and then they try to do materialized views at the end, and they never are able to make a very good implementation. If you can solve that problem, there are some really dramatic implications.  

Sabrina: Outside of enabling and applying artificial intelligence to solve real-world challenges, what do you believe will be the greatest source of technological disruption in innovation over the next five years?

George: I think that artificial intelligence is incredibly significant. I have a car that basically drives itself, a Tesla, and it’s amazing. But I don’t think it’s going to be a horizontal technology that changes every space. I think what people do with data is mostly going to be the same and is not going to be impacted by AI in the context of businesses managing their data. I think, actually, it’s going to have a lot less impact than people think.

Sabrina: And what is the most important lesson, likely from something you wish you did better, that you have learned over your startup journey?

George: When we started Fivetran, we were too early for what we would ultimately do, and we solved that problem by waiting, so timing is incredibly important, and you don’t control it. That is a big challenge for every startup. Are you too early, or are you too late? And if so, what are you going to do about it?

Sabrina: And then last, just fun question, why the company name — Fivetran — maybe it’s a play on words to Fourtran, but just curious why, why the company name?

George: Fivetran is a pun on Fortran. That goes all the way back to the original, original idea for Fivetran, which did not last very long at all, which was to make a vertically integrated data analysis tool for scientists. You know, way back, Fortran was how most scientists would write their data analysis code. So, it was a short-lived idea, but that’s where the name comes from. And, you know, we changed the idea and never changed the name, but it has turned out to be a very good name. It is reasonably memorable, and it is unique. If someone types Fivetran into Google, then they’re looking for us. And that has allowed us to measure the growth and awareness of our product using Google Trends, which has been incredibly helpful. And so, I always recommend it to founders. If you’re doing a B2B company where the name maybe isn’t that important, make sure you have something that registers a zero in Google Trends.  

Sabrina: Yeah, it’s incredibly difficult now these days with all the startup companies — people have gotten pretty unique in the company name. And it’s challenging when you pick a name that’s an adjective or noun or something that is already a word. I’ve seen some interesting ones.  

George: You want to know something really funny, and not a lot of people know this, but in the old days of Y Combinator, when Paul Graham was still there. Paula Graham was the master of coming up with names. And anytime a company had a name that was a problem, and they couldn’t get the domain they needed, or they discovered it conflicted with someone else’s name, they would send them to him. And then they would sit there in his office and figure out a new name. And he was so good at it. It’s like his secret talent. It was not needed for us, but there were some other companies in our batch that were marched off to go figure out a new name with PG.

Sabrina: That’s awesome. Coming up with that company name is one of the most challenging things. Well, thanks George for being with us here today. It’s been a lot of fun, and I appreciate you joining us and spending some time with us today.

George: Nice to be with you.

Coral: Thank you for listening to this week’s IA40 Spotlight episode of Founded & Funded. To learn more about Fivetran, visit Fivetran.com. That’s F-I-V-E-T-R-A-N.com. Thank you again for listening, and tune in in a couple of weeks for our next episode.  

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