Redpanda’s “power to the data engineer” strategy lands a $100M Series C funding round

Redpanda’s “power to the data engineer” strategy lands a  0M Series C funding round

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 shares how things work for the company.

Back in early 2022, a $50 million Series B funding round was not that unusual for a deep tech company. Today, a $100 million Series C funding round is not very common – except perhaps for generative AI startups.

Besides generative AI, what’s grabbing the headlines in the data, analytics and infrastructure world today is the Databricks vs. Snowflake rivalry. As FirstMark’s Matt Turck notes, both are vying to be the new data clouds – the most gigantic enterprise market.

Redpanda, however, suggests there’s another way. Today Redpanda announced that it has raised $100M in Series C funding led by returning investors Lightspeed Venture Partners, along with GV and Haystack VC.

We connected with CEO and founder Alex Gallego and we talked about what this signifies, how Redpanda sees the market evolving, and what’s next.

Foundations and growth

We started by asking Gallego – why another funding round, only 18 months after Redpanda’s Series B? And what made investors think that it’s a good idea to invest more money in Redpanda, when funding in general has cooled down considerably?

“In the last year alone, we grew our revenue five times and doubled the size of the company. We have not lost a deal to any competitor in the last seven months. When investors look at profiles of companies in their portfolios and they say – well, this company wins all the time, and they’re growing so fast, and the logos that they have access to are the largest companies in the world..We should help them to continue to expand that. I know this was a very difficult round for a lot of companies, but we have always been oversubscribed”, Gallego said.

Gallego then went on to elaborate on how he sees Redpanda moving forward. Released in early 2021, Redpanda is touted as a modern streaming platform that gives developers a simpler, faster, more reliable, and unified record system for real-time and historical enterprise data.

The streaming analytics market (which depending on definitions, may just be one segment of real-time data processing) is projected to grow from $15.4 billion in 2021 to $50.1 billion in 2026, at a Compound Annual Growth Rate (CAGR) of 26.5% during the forecast period as per Markets and Markets.

One thing to know about the real-time data processing market is that there is a sort of de facto standard there: Apache Kafka. We have followed Kafka and Confluent, the company that commercializes it, since 2017. Kafka and Confluent have been growing and evolving too, and in April 2021 Confluent confidentially filed for IPO.

Gallego built Redpanda on the premise of keeping what he thinks is great about Kafka – the APIs and ecosystem – while eliminating what he calls “categorical complexity”, increasing performance – tenfold, he claims – and making adoption of streaming data infrastructure more cost effective.

Key features in Redpanda’s offering is simple Ops, as there is a single binary file to deploy, and unlimited data retention, as Redpanda works directly on cloud storage such as AWS S3. Previously, Gallego stated that his vision for Redpanda is to become the “Ring Zero” – a streaming system as the source of truth.

Technical development and strategic goals

Gallego highlighted three areas of technical development for Redpanda going forward. These also happen to be key enablers for Redpanda’s strategy, so they deserve some analysis.

First, support for Wasm. Gallego claimed Redpanda was the first streaming platform to enable Web Assembly in 2020. He conceded Wasm support is not production-ready at the moment, but the goal is to invest heavily in it to get there. The reason, he said, is strategic.

“When you look at Gen AI models or all the data and machine learning workloads, the bulk of the engineer’s time is spent cleaning up the data. Web Assembly allows us to just clean it up in the storage engine rather than ping-ponging your data back and forth between multiple systems”, Gallego noted.

Wasm has reportedly been used for faster and more efficient data processing closer to the source. Therefore, it sounds like enabling data processing and transformation to happen using Wasm at the point of entry could indeed result in better performance.

Redpanda, as opposed to some of its streaming platform competitors, does not support complex stream processing via SQL directly, only via its partner ecosystem. But enabling complex stream processing via Wasm before the competition could pay off.

The second new direction for Redpanda, which Gallego called “most fundamental and different philosophically”, is changing Redpanda’s cold storage to open-source Apache Iceberg. Gallego believes that’s a huge market differentiator, because it enables organizations to regain control of their data.

“Today, if you’re a company and you send your data to, say, Snowflake, the way to carry back your data is to use the Snowflake API. In a way, your data is in a walled garden, and it’s how Snowflake makes money. It’s a great business model by revenue numbers. But to the engineers, it just doesn’t make any sense. You should own the data. Intuitively to us, it makes sense. If I generate my data, then I should own the data entirely.

That’s why Redpanda inverts the relationship with data vendors. Instead of us owning your data in our format, which is what we do today, we’re starting to work on the development of rebuilding the storage engine so that the format is in Apache Iceberg so that you can plug in any downstream query engine”, Gallego said.

Go to market strategy

Indeed, that sounds like a “power to the people” strategy, or rather to the organizations that generate and own the data. That chimes in well with how Gallego previously described Redpanda’s target audience: “streaming experts that want something more with their storage”.

While this bottom-up go to market strategy has worked well for Redpanda, it does not work in isolation. Bottom-up is for awareness and adoption, aided by Redpanda’s source-available BSL license, sales works in the traditional top-down way.

“To me, the hero of this story is the builder, the maker, the doer, who’s actually putting products together and ideas into working systems. We deliver things that made no sense in some people’s mind for the market, like the idea of BYOC where we don’t get to charge for reselling Amazon compute. That’s okay. I want to charge for the value we deliver to people.

Same thing with Apache Iceberg. They’re like, Hey, but you’re not locking them in. I was like, That’s what the engineer wants. We have to lean towards making that person the hero of this story. I think if we do that well, we’ll be a massive financial success and an iconic company”, Gallego said.

As Gallego put it, Redpand has two buyers: the practitioner and then the executive sponsor. Redpanda is aiming for adoption by the practitioner via its engineering, and aiming for sales at the executive sponsor via its low TCO.

Last but not least, Redpanda will continue to invest in serverless. The goal is to lower the barrier to entry for engineers who are not very experienced in streaming, giving them a cost effective solution to get started.

Generally, Redpanda relies a lot on its large ecosystem of partners for things that do not fall within what the team sees as core streaming functionality. This is how Gallego also approaches capabilities such as graph processing or vector search. While he acknowledges these are seeing growing adoption, he is happy to pass those opportunities on to partners.

Redpanda is taking some interesting bets. If the momentum keeps up and the thinking does not change, who knows, we may see a $200 million Round D in another 18 months.

Disclosure: The author has worked with Redpanda in the past.

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