Celonis, the late-stage process mining software startup, announced a $1 billion Series D investment this morning on an eye-popping $11 billion valuation, up from $2.5 billion in its Series C in 2019, quadrupling its value in just two years.

Durable Capital Partners LP and T. Rowe Price Associates co-led the round, with participation from new investors Franklin Templeton, Splunk Ventures and existing investors Arena Holdings. Other unnamed existing investors also participated.

While it was at it, the company announced it was naming experienced financial pro Carlos Kirjner as CFO. Kirjner’s most recent job was at Google, where he led finance for ads and other key product areas, according to the company.

The presence of institutional investors like T. Rowe Price and Franklin Templeton and the huge influx of capital could be a signal that this is the last private fundraise for the company before it goes public, and Celonis CEO and co-founder Alexander Rinke did not shy away from IPO talk when asked about it.

“It could be, yeah. It’s kind of tough to predict the future, but look, we’re very bullish about the growth and our prospects both as a private — and down the road — a public company, and obviously we now have backers that can invest capital in both [public and private markets],” Rinke told TechCrunch.

Rinke says what’s driving this interest is the tremendous potential of the market even beyond process mining, which he sees as just a starting point for a much larger market. “Process mining where we originated from is really just the gateway to build new processes and better processes for organizations, and as you think about that that’s a much much bigger market that we’re addressing,” he said.

The company’s processing mining software sits at the beginning of the process automation food chain, which includes robotic process automation, no-code workflow and other tools to bring more automated workflows to companies. It’s quite possible that the company could develop other pieces of this or use the new capital to buy talent and functionality, something that Rinke acknowledges is possible now with this much capital behind the company.

Celonis started by mapping out exactly how work flows through an organization, something that used to take high-priced human consultants months to figure out sitting with employees and watching how work flows. Once a company knows how work moves through an organization, it’s easier to find inefficiencies and places that are ripe for using automation tools. Speeding up that first part of the operation with technology can bring down the cost and accelerate innovation and change.

The company made a huge deal with IBM recently where IBM plans on training 10,000 consultants worldwide to use Celonis tooling. That brings the power of a company the size of IBM to one that is still relatively small in comparison — Rinke thinks they’ll reach 2,000 employees by year end — and that could be at least part of the reason investors were willing to pump so much capital into the company.

The company, which recently turned 10, currently has 1,000 enterprise customers, including Uber, Dell, Splunk (which is also an investor), L’Oréal and AstraZeneca.

Take a close look at any ambitious startup and you’ll find pugnacity nestled in its core. Stubbornness and a bullheaded belief in the worth of what a company wants to bring to fruition is often the biggest driver of its success, and the people at such companies also tend to share this quality.

So it wouldn’t be too far off the mark to say the people at Expensify are a stubborn lot — to the company’s ultimate benefit. This group of P2P pirates/hackers that set out to build an expense management app stuck to their gut, made their own rules. They asked questions few thought of, like: Why have lots of employees when you can find a way to get work done and reach impressive profitability with a few? Why work from an office in San Francisco when the internet lets you work from anywhere, even a sailboat in the Caribbean?

It makes sense in a way: If you’re a pirate, to hell with the rules, right? And even more so when nobody can explain the rules in the first place.

With that in mind, one could assume Expensify decided to ask itself: Why not build our own totally custom tech stack? Indeed, Expensify has made several tech decisions that were met with disbelief — from having an open-source frontend and cross-platform mobile development to hiring contractors to train its AI and recruiting open-source contributors — but its belief in its own choices has paid off over the years, and the company is ready to IPO any day now.

How much of a tech advantage Expensify enjoys owing to such choices is an open question, but one thing is clear: These choices are key to understanding Expensify and its roadmap. Let’s take a look.

Built on Bedrock

I think another question Expensify also decided to ask in its early days was something like: Why not have our database on top of a technology that’s built for small-scale application software?

It may sound incredible, but Expensify actually runs on a custom database built on top of SQLite. This is surprising, because despite being one of the most widely deployed database engines, SQLite is known for running on small, embedded systems like smartphones and web browsers, not powering enterprise-scale databases.

This custom database is called Bedrock, and its architecture is as unique as they come. Expensify explains it as an “RDBMS optimized for self-healing replication across relatively slow, relatively unreliable WAN (internet) connections, enabling extremely high availability/high performance multi-datacenter deployments without any single point of failure.” RDBMS means relational database management system, describing SQLite and other row-based databases where entries are interconnected with each other.

But why would Expensify build this instead of going for any number of widely available enterprise database solutions?

To answer that question, we need to go back to the early days of the company, which was originally a side project for its founder and CEO, David Barrett. His initial idea was to develop a prepaid card for the homeless, but this required putting a server on the Visa network, which brought several strict requirements and challenges. “I would say one of the most difficult [parts] was that I needed the ability to automatically replicate and failover,” Barrett told TechCrunch when we interviewed him a couple of months ago.

This was no easy feat in 2007, but Barrett was up for the challenge. “I just hit a moment where the technology available off the shelf just wasn’t that good. And I happened to be a peer-to-peer software developer who had tons of spare time and really wanted to build this thing to put on the Visa backend,” he said. The P2P aspect was important, as Barrett had the skills to make it work. His first hires for Expensify, P2P engineers he had worked with at Red Swoosh and Akamai, were also unusually suited for the job.

Iterative, an open-source startup that is building an enterprise AI platform to help companies operationalize their models, today announced that it has raised a $20 million Series A round led by 468 Capital and Mesosphere co-founder Florian Leibert. Previous investors True Ventures and Afore Capital also participated in this round, which brings the company’s total funding to $25 million.

The core idea behind Iterative is to provide data scientists and data engineers with a platform that closely resembles a modern GitOps-driven development stack.

After spending time in academia, Iterative co-founder and CEO Dmitry Petrov joined Microsoft as a data scientist on the Bing team in 2013. He noted that the industry has changed quite a bit since then. While early on, the questions were about how to build machine learning models, today the problem is how to build predictable processes around machine learning, especially in large organizations with sizable teams. “How can we make the team productive, not the person? This is a new challenge for the entire industry,” he said.

Big companies (like Microsoft) were able to build their own proprietary tooling and processes to build their AI operations, Petrov noted, but that’s not an option for smaller companies.

Currently, Iterative’s stack consists of a couple of different components that sit on top of tools like GitLab and GitHub. These include DVC for running experiments and data and model versioning, CML, the company’s CI/CD platform for machine learning, and the company’s newest product, Studio, its SaaS platform for enabling collaboration between teams. Instead of reinventing the wheel, Iterative essentially provides data scientists who already use GitHub or GitLab to collaborate on their source code with a tool like DVC Studio that extends this to help them collaborate on data and metrics, too.

Image Credits: Iterative

“DVC Studio enables machine learning developers to run hundreds of experiments with full transparency, giving other developers in the organization the ability to collaborate fully in the process,” said Petrov. “The funding today will help us bring more innovative products and services into our ecosystem.”

Petrov stressed that he wants to build an ecosystem of tools, not a monolithic platform. When the company closed this current funding round about three months ago, Iterative had about 30 employees, many of whom were previously active in the open-source community around its projects. Today, that number is already closer to 60.

“Data, ML and AI are becoming an essential part of the industry and IT infrastructure,” said Leibert, general partner at 468 Capital. “Companies with great open-source adoption and bottom-up market strategy, like Iterative, are going to define the standards for AI tools and processes around building ML models.”

When Cloudera announced its sale to a pair of private equity firms yesterday for $5.3 billion, along with a couple of acquisitions of its own, the company detailed a new path that could help it drive back toward relevance in the big data market.

When the company launched in 2008, Hadoop was in its early days. The open-source project developed at Yahoo three years earlier was built to deal with the large amounts of data that the internet pioneer generated. It became increasingly clear over time that every company would have to deal with growing data stores, and it seemed that Cloudera was in the right market at the right time.

And for a while things went well. Cloudera rode the Hadoop startup wave, garnering a cool billion in funding along the way, including a stunning $740 million check from Intel Capital in 2014. It then went public in 2018 to much fanfare.

But the markets had already started to shift by the time of its public debut. Hadoop, a highly labor-intensive way to manage data, was being supplanted by cheaper and less complex cloud-based solutions.

“The excitement around the original promise of the Hadoop market has contracted significantly. It’s incredibly expensive and complex to get it working effectively in an enterprise context,” Casey Aylward, an investor at Costanoa Ventures told TechCrunch.

The company likely saw that writing on the wall when it merged with another Hadoop-based company, Hortonworks, in 2019. That transaction valued the combined entity at $5.2 billion, almost the same amount it sold for yesterday, two years down the road. The decision to sell and go private may also have been spurred by Carl Icahn buying an 18% stake in the company that same year.

Looking to the future, Cloudera’s sale could provide the enterprise unicorn room as it regroups.

Patrick Moorhead, founder and principal analyst at Moor Insight & Strategies, sees the deal as a positive step for the company. “I think this is good news for Cloudera because it now has the capital and flexibility to dive head first into SaaS. The company invented the entire concept of a data life cycle, implemented initially on premises, then extended to private and public clouds,” Moorhead said.

Adam Ronthal, Gartner Research VP, agrees that it at least gives Cloudera more room to make necessary adjustments to its market strategy as long as it doesn’t get stifled by its private equity overlords. “It should give Cloudera an opportunity to focus on their future direction with increased flexibility — provided they are able to invest in that future and that this does not just focus on cost cutting and maximizing profits. Maintaining a culture of innovation will be key,” Ronthal said.

Which brings us to the two purchases Cloudera also announced as part of its news package.

If you want to change direction in a hurry, there are worse ways than via acquisitions. And grabbing Datacoral and Cazena should help Cloudera alter its course more quickly than it could have managed on its own.

“[The] two acquisitions will help Cloudera capture some of the value on top of the lake storage layer — perhaps moving into different data management features and/or expanding into the compute layer for analytics and AI/ML use cases, where there has been a lot of growth and excitement in recent years,” Aylward said.

Chandana Gopal, research director for the future of intelligence at IDC, agrees that the transactions give Cloudera some more modern options that could help speed up the data-wrangling process. “Both the acquisitions are geared towards making the management of cloud infrastructure easier for end-users. Our research shows that data prep and integration takes 70%-80% of an analyst’s time versus the time spent in actual analysis. It seems like both these companies’ products will provide technology to improve the data integration/preparation experience,” she said.

The company couldn’t stay on the path it was on forever, certainly not with an activist investor breathing down its neck. Its recent efforts could give it the time away from public markets it needs to regroup. How successful Cloudera’s turnaround proves to be will depend on whether the private equity companies buying it can both agree on the direction and strategy for the company, while providing the necessary resources to push the company in a new direction. All of that and more will determine if these moves pay off in the end.