Orchestrate all the Things - Redpanda’s “power to the data engineer” strategy lands a $100M Series C funding round. Featuring Founder / CEO Alex Gallego

Episode Date: June 27, 2023

In an era of dried-up funding and Data Lakehouse vendor supremacy, Redpanda is going against the grain. The company just secured a $100 million Series C funding round to execute on an unconventional strategy. Redpanda Founder and CEO Alex Gallego explains how things work for the company. Article published on Orchestrate all the Things

Welcome to Orchestrate All The Things. I'm George Anadiotis and we'll be connecting the dots together. Stories about technology, data, AI and media and how they flow into each other, saving our lives. In an era of dried-up funding and data lakehouse vendor supremacy, Red Panda is going against the grain. The company just secured a 100 million series C funding round to execute on an unconventional strategy. Red Panda founder and CEO Alex Gallego explains how things work for the company. I hope you will enjoy the podcast. If you like my work, you can follow Link Data Registration on Twitter, LinkedIn, and Facebook. Alex, founder and CEO of Red Panda. I am also the original engineer, hacker, author of the Red Panda project, so the code base where we built the company around it. And I've been doing

streaming for about 14 years. Prior to building Red Panda, I sold a company to Akamai in 2016. That part of some large use cases there. I started this company in 2019. And so the company is four years, one and 1.1 quarters old, 1.5 quarters old. You know, we really have been selling the product for the last two years and one and a half quarters. Prior to that, we spent the first two years of the company, you know, rethinking how the future should be. And so we built Red Panda with three core tenets. One was simplicity, and it's probably the most fundamental reason why developers love

Red Panda. It is so, so easy to use. We really obsess over the developer experience. I almost lose sleep on it. It's kind of unhealthy relationship for me, but it's true. It's really why developers love Red Panda. And so that's always the first one. The second one, pillar of the product was performance. And so typically, you know, we see use cases where we deliver, I know you cover Scylla as well, right, where we see common practice to see, you know, 10x lower latencies. And those in some commercial use cases actually deliver, you know, 10x lower hardware footprint that we i have personally seen from large use cases right so we took one use case for example from 400 computers down to 40 um this is for a large public company in the u.s so the first one simplicity second was performance

um probably performance is what people know us most for today and simplicity. And then the last one is cost-effectiveness. And that comes from having built a disaggregation of computer and a store. So we learned the best thing from Kafka, which was the API, and we learned the best thing from Pulsar, which was the disaggregation of compute and store. And so our true storage is S3 or Google Cloud Bucket or whatever so-called object storage system. And that in turn delivers 6X, 4X cost reduction from people. In fact, we have an active campaign on the website where we say we'll take any Confluent bill and cut it by 50% or I will give them money,

which is, I feel really, really strongly about it. And so those were the three core tenets of why we originally started the technology and we've been you know super successful uh in that regard we have companies like uh activision akamai cisco lacework i've ran mid journey which on this is a you know hugely popular ai company booties ncr optimizely texas instruments vodafone stonex which is a fortune 100 um you know the the one of the third largest sports leagues in the US, and so on. And so we've managed to port over the largest streaming workloads for the most demanding use cases over onto Red Panda. Let me pause here for a second, because I've been talking for four minutes.

I make sure I don't lose you. Yeah, I'm still here. And I was just thinking to myself, well, you're basically sort of summarizing what we talked about the previous time we connected, which was about a year and a half ago. Except for the new bits, like, okay, not everything of what you said was known back then, but I think the core tenets at least have stayed pretty much the same.

And so just to provide some context, the occasion today is the fact that you're about to announce round C funding. And this is kind of similar to the last time we connected, actually. The only difference being that, well, back then it was your series B and now it's series C. So it's a typical thing for me to ask myself about and then actually ask the people I'm having the conversation with about.

It's a kind of dual-pronged, let's say, question. So why did you think that raising another round was needed for you? And what did you use to actually convince your investors that this is a good idea and also to add to that fact well your round your series b was not that long ago and it wasn't small either if my memory is right was something like 50 million that's correct that's correct okay so you raised you raised a pretty big round and now you've actually raised an even bigger one. I think it's over 100 million this time within a relatively short period of time. So why did you feel that you needed to do that? Great question. In numbers, in the last year alone, we quintupled the revenue, we doubled the size of the company, and we have not lost the deal to any competitor in the last seven months.

And so when investors look at profiles of companies in their portfolios and they say like, well, this company wins all the time and they're growing so fast. And the kind of logos that they have access to are the world's largest companies in the world. Like we should put fire in, in, you know, I guess, wood in the fire, whatever the American analogy is, to, to continue to expand that. And so for me, I know this was a very difficult uh round for for a lot of companies you know but but we have always been oversubscribed um do you know we continue to always have up rounds in in in the companies and so uh it made sense for us to have a large war chest, if you will, to grow our go-to-market, right?

So now that we are a mature company that we can tackle the most demanding workloads, we now have true enterprise features, large companies are adopting us, we deliver TCO, but we still work with their complicated enterprise environments and so on. Now's the time to accelerate the growth. And so this wasn't anything other than like,

we just want to continue to accelerate the growth. We have a great opportunity ahead of us from a company and an investment perspective. And when you look at those numbers, you just, it's obvious, right? And it's why the round is so large and it continues to be an up round. And the numbers speak now from a technology perspective,

which is the second question. So that's like why people invest in SRQ, just kind of makes sense. From a technology perspective, it gives me the opportunity to continue to deliver what I think is the future of streaming data. And I think the future, so today I feel that Red Panda is the best present

of streaming data with the features that we have today. I mentioned Kafka compatibility, disaggregation of compute on a store. The future for us is different from how everyone else sees the future of streaming data, different from Snow, different from Databricks, different from how everyone else sees the future of streaming data. Different from Snow, different from Databricks, different from Confluent. And it's based on really three pillars.

And so the first one is we were the first streaming and storage engine back in 2020 to enable WebAssembly. It was really mostly of a prototype idea of like, right? So we really want to invest heavily in WebAssembly. It's really mostly of a prototype idea of like, right? So we really want to invest heavily in WebAssembly from a company strategic direction perspective. The reason for that is that when you look at, you know, whether it's GenAI models or anything at their all the data ML workloads,

the bulk of the engineer's time is spent cleaning up the data. And so WebAssembly allows us to just clean it at the storage engine rather than ping-ponging your data back and forth between multiple systems as first. The second one, and perhaps most fundamental, and like different philosophically, et cetera, is to change our true storage engine

in cold storage to be Apache Iceberg. And so that's a huge market differentiator. It's very different philosophically. Let me explain why. Today, if you're a company and you send your data to Snowflake, the way to query back your data is to use the Snowflake. In a way, you sort of wall garden your data and it's how they make money, right? So it's a great business model by revenue numbers, right? But to the engineers, it just doesn't make any sense.

Like, you know, you should own the data. Like intuitively to us, it makes sense. Like if I generate my data, then I should own the data entirely. And so that's what Red Panda sort of inverts the relationship with data vendors where, hey, instead of us owning your data in our format, which is what we do today, right? We're starting to work on the development of rebuilding the storage engine so that the format is an Apache Iceberg so you can plug in any downstream query engine. And so that's probably the most significant technology investment for us that's going to

be expensive and it's going to take time. But we want to invest in that. I think that's where we see the future. And then the last one is server is serverless is to continue to invest in serverless so that we lower the barrier to entry for the young engineer, right? Someone who is not a late stage career engineer, who's just starting, is facing a problem. We give them a super cost effective solution to get started. So those are the three core pillars, web assembly, Apache Iceberg, and serverless. Does that make sense? Yeah, that's interesting. Actually, I've had the chance to go through your draft press release and those technical items in terms of the direction you want to take the product sort of relisted out. So I was going to ask you

to elaborate on those anyway. But now that you have, I'm starting to see something that goes a little bit beyond those technical capabilities per se. I'm starting to see some elements, let's say, of your strategy as a company. So the WASM, when you talked about WASM and how it actually enables you to work closer to where the data is and avoid just having to pass it around. That, to be perfectly honest, I've never actually used Wasm myself, but this is what I've seen engineers who actually do use it praise it for. So in that respect, it sounds like, well, working on this support would actually enable you to try and not exactly go after, I wouldn't frame it in this context, but let's say eat up a little bit of the usage of solutions like Databricks.

So if people are using streaming anyway, and in that case, specifically, people are using Red Panda anyway, by enabling them to use something like Basm for data transformation, you're basically telling them, well, maybe you don't need to pass at least all your data to something like Databricks. You can work on your data transformation already within Red Panda. Similar with Iceberg. So same idea, different external systems. So we're like, okay, so maybe you don't need to pass your data to Snowflake or whatever

other data warehouse orflake or whatever other data warehouse or lake house or whatever. Maybe you can just keep storing it in Red Panda. And I know this is something that you've always sort of highlighted from the early days, precisely because you use S3 and all the other cloud storage formats. You're sort of encouraging people to use Red Panda as their primary data store. And by adding Iceberg support, you're making this, I guess, a bit easier and even more, well, portable, let's say, across formats. So besides the actual, I guess, I mean, serverless, as you also pointed out, it sort of makes it easier to onboard people who are not necessarily proficient, let's say, with all the other technologies. So the combination of those three technical elements makes it seem like, well, plus the fact that you just got quite substantial funding makes it pretty obvious where you want to

go with that basically yeah i i think so uh thanks for recapping that was that was uh excellent don and you nailed it uh you nailed you nailed that the way i think about this though is if i make the engineer hands-on keyboard behind the terminal successfully if i make that person successful will be a massive financial success but to me that's the hero of this story is the builder you know the maker the doer who's actually putting products together and and you know ideas into working systems that that's the person i've always been trying to make the hero. And, you know, we deliver things that made no sense for in some people's mind for the market, like the idea of BYOC, where we don't get to charge for reselling Amazon compute. And I was like, that's okay. I want to

charge for the value we deliver to people. Right. And so same thing with Iceberg, they're like, Hey, but you know, you're not locking them in. I was like, that's what the engineer wants. You know, we have to lean towards making that person the hero of this story. And I think if we do that well, we'll be a massive financial success and an iconic company. Okay, so that already answers implicitly another question I had for you, which had to do with your go-to-market strategy, basically. So I remember from the previous time we spoke that you specifically framed Red Panda as something that you want streaming experts to use that want to do something more with the storage. So basically having like a bottom up,

let's say, go-to-market strategy aiming for technical people and then I guess technical decision makers rather than, I don't know, C-suite or middle management or whatever. And it sounds like that hasn't really changed. So I think it changed a little bit though. For us, it's really focused on, on awareness.

So our adoption does come from the bottoms up, but people don't pay us money for it. So our sales process. That precisely was going to be my question. So, and. How did you basically convince your investors that, well, you know, we're just going to go bottom up and this is going to work out great because I think they usually expect at least to see those two, like bottom up and top down strategies playing out in parallel. Yeah. So that's, we make, we made a pivot last year to do that, where we have two parallels and so the product and marketing engine is around awareness right we make um ultimately what i said before is true if you make this person successful the

builder then we'll be financial because how we capture that financial success is that we focus on on awareness and enablement it's like hey let me help you build your applications. This is some example. This is some templates. Let me help you walk through with classes, with webinars, with a bunch of enablement for the engineer. And we pair that with a top-down strategy. And why does it work for us is we deliver an insane TCO. Like, you know, it's often 4x cost of alternatives for the, one of the largest

Indian social media company, we saved them 10 million bucks for, you know, one of the largest analytics company in the US, we saved them three and a half million dollars. This is like savings after licensing and cost and hardware spend. And so we save so much money that is fiscally responsible for the CIO not to choose Red Panda. And so that's kind of how we pair it, right? We pair it with awareness, but we have, you know, really two buyers, the practitioner and then the executive sponsorship and you know my my marketing and my product largely focuses on like uh awareness and enablement of the engineers being successful with the product and that's worked out fantastic you know just in numbers like i mentioned early on is we just quintupled revenue last year and we doubled the size of the company so that seems to work out great in this

economic environment where cost is top of mind for all of the CIOs. Yeah, I have to say this sort of framing is something I've seen with other vendors in your space as well. Not necessarily streaming strictly, but the broader data space as well. So if you have an offering that makes life easier on the ground, like, you know, less hustle, therefore, less time to complete a project and less cost in infrastructure, that sort of translates in savings for the decision makers. And this sort of framing plays out well on both ends, let's say. Exactly. It's like one of the solutions where we all win. The executive looks really smart because he saves a bunch of money

and the developer loves us. And, you know, we win because we help them too. Oh, okay. So speaking about the broader streaming space, I was wondering if we could wrap up by just picking up your brain, basically, on what do you see going on today in that space? I mean, OK, besides the obvious, you know, like market analysis that shows that the space is growing. What do you see, like in terms of technical development, where do you see this space going?

And as a specific question to address, I'd like to ask you about, well, if you see people applying graph analytics in real time data. A long, long time ago, I spoke to some people from Spark and they have like graphics and graph frames. And at the time, it was like six or seven years ago, they were kind of timid about it. They were like, yes, we have those, but not that many people are using. So I'm just wondering if you have something similar and what do you see the response to that. Great question. Let me think so I can give you a good answer. Yeah, sure.

We see a massive tailwind from artificial intelligence push on streaming data, which is different from three years ago. And graph databases and other databases morphed a little bit into vector databases right so it's similar problems of lookup space you know you're looking for

numbers so floating numbers floating you know integers sequences and so on as opposed to like people right like social not not so much social networks in terms of graph but but really networks in terms of numbers and coefficients um and so i think similar technology techniques used in a slightly different context in the context of artificial intelligence and that tailwind has been a really strong force for us. And I think I continue to, so if I close my eyes and I look at next year, I think over the next two to three years, streaming, something that looks like streaming will be what I think is the ring zero. So the building block for future enterprise applications. Let me explain. Something that looks like Red Panda

has to be the messaging layer that connects your web serving layer with your ML layers. And you need something that looks like that. So you can add databases on top. One database could be vector search. One database could be search like the recent acquisition from Snowflake and so on. You can add data storages on top, but you need something that is low cost, that is massively scalable, that has a huge ecosystem play so that when you connect TensorFlow

to something that looks like Red Panda, you can get value in five minutes. And so that's kind of how I see the world. And I think a big push for that has been the pressure on systems to store more data and render the data faster in some capacity, right? Like those two pushes make Red Panda, you know, gives us this huge tailwind. And since I'm focused on streaming, most of the people that I talk to is that. And so, you know, obviously take it with a grain of salt.

This is like my frame of glasses, you know is where where i sit in the infrastructure stack um uh but yeah it's just it's really interesting that the kind of idea of model stacking for example plays really well with the streaming system where like you know you send your data to one stream and then you it's one model and then the model produces back another stream and then you can see another model on top. And so it sort of gives people infinite flexibility of model stacking, of experimentation, of replay. And so it's a very convenient API for developers to build applications on top. And I continue to see that as a strong tailwind for us. Interesting.

Yeah. And, um, since you mentioned actually mentioned actually vector capabilities, I agree. This is also something that I see happening a lot these days. Many database vendors adding vector capabilities, not just graph databases, but databases all over the place do that. And I wonder, do you see people using that a lot? Do you see even people asking a vendor like Red Panda to do that? Or in that case, do you just say, well, we'll just stream the data for you, but then you just use your backend of choice for that?

Yeah, we have a lot. I think people look at Red Panda for technical advice, not just on how to build their streaming architecture, but how to build their applications. And the reason is we're such a, you know, tier zero, like critical infrastructure for people that they're like, what databases do you recommend we use for this other critical? And so, you know, they ask us and we're not going to build. We have our hands busy trying to advance our technology as we are today. But we just tend to recommend, you know, depending on the use case, depending on whatever technologies they have. And so we partner extremely well with people. Basically, everyone that we've partnered with with we'd help them make money uh

really we bring a lot of people on and so we have a super large and thriving partner ecosystem i mean i would say tens and tens of partners i need to come up with with the right number um but yeah we just partner really well with people and so i would rather focus is i think it's a superpower for a company and and i don't want to distract myself and and the company from our true mission and so we just partner well when we recommend other databases uh for for people but people ask us all the time we're just not you know i think it would be too distracting for me to to go and build that. Yeah, I think that makes sense. To be honest, I hadn't checked your partner ecosystem previously and just listening to you elaborate on how you deal with the situation, I was just thinking to myself, well, it sounds

like you have a lot of power, actually, the way you describe it. So again, it sounds like it would be a good idea for other vendors to partner with you in that sense. If you can give a recommendation, well, if I'm a vendor and I know that you have this kind of power, I would definitely like to partner with you. Yeah. I mean, you nailed it.

The number, we probably get three major vendor partners. You know, I'm talking about like, if you like, this car, some of the early, early stage companies, like probably three or four a week, just trying to, you know, set up advanced conversation. And, and we feel, you know, really lucky to be in that position. And people trust us, like, the thing is, is trust is earned, and people trust us because we give them true value. And they're like, wait, like help me. Like, how do you see the world? And so just like this conversation, it tends to be very natural. And, and so we, we,

our aim is to continue to make that person the hero. I was like, we just want to help you be successful because we know if we make them successful and Red Planet is part of that, it doesn't have to be all of it. It just has to be part of that. Then we'll be a massive financial success. And, and that, that's how i sort of see our relationship it's like i just want to help them um and and you know we capture some financial value out of that we don't have to capture all of it but we capture some yeah and well uh again it sounds like at least a good a good part of your new hires probably goes through to partner management since you have all those partners

yeah yeah new hires i mean our go-to market is scaling sellers pre-sellers customer success post-sales you know like uh it's not enough to just sign someone like i think you know it's sort of the entire customer journey for me it is truly a long-term partnership we're and we're we're like you know tier zero as as it comes from some of the customers i mentioned and so if we go down their business goes down lacework for example do you know they posted a youtube they wrote a new stock article we're happy to send you but like just to give you a sense of like the the kind of criticality for them and so we really try to see ourselves as like a long-term partner so it's not just enough to like hey sign the contract pay me money

it's like let me help you through the life of this journey let me you know involve you in the roadmap discussion what do you think about how do we think about the roadmap is this something that you know would be helpful to you um and so on and so it's it's for me it's almost easy uh to have like an influx of idea of what to build. The hard thing, as you may know, is like saying no. I say no mostly. I know we're two minutes over.

I have to jump to my next call, but I just wanted to make sure that I answer all of your questions. Does that make sense? Yeah, yeah, perfectly. And yeah, thank you very much for your time. I know you have a lot to do, especially today. So good luck with everything. And well, let's see what happens in the next 18 months. I'm curious as to whether there'll be like a round D, I guess. Well, if that keeps up,

your round D will probably be over 200 million. But well, let's wait and see what happens. Yeah, if all things continue to work. George, great chatting with you. Great to catch up again. Thanks for sticking around. For more stories like this, check the link in bio and follow linked data orchestration.