Personal blog of Yzmir Ramirez

Databricks this morning confirmed earlier reports that it was raising new capital at a higher valuation. The data- and AI-focused company has secured a $1.6 billion round at a $38 billion valuation, it said. Bloomberg first reported last week that Databricks was pursuing new capital at that price.

The Series H was led by Counterpoint Global, a Morgan Stanley fund. Other new investors included Baillie Gifford, UC Investments and ClearBridge. A grip of prior investors also kicked in cash to the round.

The new funding brings Databricks’ total private funding raised to $3.5 billion. Notably, its latest raise comes just seven months after the late-stage startup raised $1 billion on a $28 billion valuation. Its new valuation represents paper value creation in excess of $1 billion per month.

The company, which makes open source and commercial products for processing structured and unstructured data in one location, views its market as a new technology category. Databricks calls the technology a data “lakehouse,” a mashup of data lake and data warehouse.

Databricks CEO and co-founder Ali Ghodsi believes that its new capital will help his company secure market leadership.

For context, since the 1980s, large companies have stored massive amounts of structured data in data warehouses. More recently, companies like Snowflake and Databricks have provided a similar solution for unstructured data called a data lake.

In Ghodsi’s view, combining structured and unstructured data in a single place with the ability for customers to execute data science and business-intelligence work without moving the underlying data is a critical change in the larger data market.

“[Data lakehouses are] a new category, and we think there’s going to be lots of vendors in this data category. So it’s a land grab. We want to quickly race to build it and complete the picture,” he said in an interview with TechCrunch.

Ghodsi also pointed out that he is going up against well-capitalized competitors and that he wants the funds to compete hard with them.

“And you know, it’s not like we’re up against some tiny startups that are getting seed funding to build this. It’s all kinds of [large, established] vendors,” he said. That includes Snowflake, Amazon, Google and others who want to secure a piece of the new market category that Databricks sees emerging.

The company’s performance indicates that it’s onto something.

Growth

Databricks has reached the $600 million annual recurring revenue (ARR) milestone, it disclosed as part of its funding announcement. It closed 2020 at $425 million ARR, to better illustrate how quickly it is growing at scale.

Per the company, its new ARR figure represents 75% growth, measured on a year-over-year basis.

That’s quick for a company of its size; per the Bessemer Cloud Index, top-quartile public software companies are growing at around 44% year over year. Those companies are worth around 22x their forward revenues.

At its new valuation, Databricks is worth 63x its current ARR. So Databricks isn’t cheap, but at its current pace should be able to grow to a size that makes its most recent private valuation easily tenable when it does go public, provided that it doesn’t set a new, higher bar for its future performance by raising again before going public.

Ghodsi declined to share timing around a possible IPO, and it isn’t clear whether the company will pursue a traditional IPO or if it will continue to raise private funds so that it can direct list when it chooses to float. Regardless, Databricks is now sufficiently valuable that it can only exit to one of a handful of mega-cap technology giants or go public.

Why hasn’t the company gone public? Ghodsi is enjoying a rare position in the startup market: He has access to unlimited capital. Databricks had to open another $100 million in its latest round, which was originally set to close at just $1.5 billion. It doesn’t lack for investor interest, allowing its CEO to bring aboard the sort of shareholder he wants for his company’s post-IPO life — while enjoying limited dilution.

This also enables him to hire aggressively, possibly buy some smaller companies to fill in holes in Databricks’ product roadmap, and grow outside of the glare of Wall Street expectations from a position of capital advantage. It’s the startup equivalent of having one’s cake and eating it too.

But staying private longer isn’t without risks. If the larger market for software companies was rapidly devalued, Databricks could find itself too expensive to go public at its final private valuation. However, given the long bull market that we’ve seen in recent years for software shares, and the confidence Ghodsi has in his potential market, that doesn’t seem likely.

There’s still much about Databricks’ financial position that we don’t yet know — its gross margin profile, for example. TechCrunch is also incredibly curious what all its fundraising and ensuing spending have done to near-term Databricks operating cash flow results, as well as how long its gross-margin adjusted CAC payback has evolved since the onset of COVID-19. If we ever get an S-1, we might find out.

For now, winsome private markets are giving Ghodsi and crew space to operate an effectively public company without the annoyances that come with actually being public. Want the same thing for your company? Easy: Just reach $600 million ARR while growing 75% year over year.

UiPath came seemingly out of nowhere in the last several years, going public last year in a successful IPO during which it raised more than $527 million. It raised $2 billion in private money prior to that with its final private valuation coming in at an amazing $35 billion. UiPath CEO Daniel Dines will be joining us on a panel to discuss automation at TC Sessions: SaaS on October 27th.

The company has been able to capture all this investor attention doing something called robotic process automation (RPA), which provides a way to automate a series of highly mundane tasks. It has become quite popular, especially to help bring a level of automation to legacy systems that might not be able to handle more modern approaches to automation involving artificial intelligence and machine learning. In 2019 Gartner found that RPA was the fastest growing category in enterprise software.

In point of fact, UiPath didn’t actually come out of nowhere. It was founded in 2005 as a consulting company and transitioned to software over the years. The company took its first VC funding, a modest $1.5 million seed round, in 2015, according to Crunchbase data.

As RPA found its market, the startup began to take off, raising gobs of money, including a $568 million round in April 2019 and $750 million in its final private raise in February 2021.

Dines will be appearing on a panel discussing the role of automation in the enterprise. Certainly, the pandemic drove home the need for increased automation as masses of office workers moved to work from home, a trend that is likely to continue even after the pandemic slows.

As the RPA market leader, he is uniquely positioned to discuss how this software and other similar types will evolve in the coming years and how it could combine with related trends like no-code and process mapping. Dines will be joined on the panel by investor Laela Sturdy from CapitalG and ServiceNow’s Dave Wright, where they will discuss the state of the automation market, why it’s so hot and where the next opportunities could be.

In addition to our discussion with Dines, the conference will also include Databricks’ Ali Ghodsi, Salesforce’s Kathy Baxter and Puppet’s Abby Kearns, as well as investors Casey Aylward and Sarah Guo, among others. We hope you’ll join us. It’s going to be a stimulating day.

Buy your pass now to save up to $100. We can’t wait to see you in October!

Is your company interested in sponsoring or exhibiting at TC Sessions: SaaS 2021? Contact our sponsorship sales team by filling out this form.

As the use of AI has grown and developed over the last several years, companies like Salesforce have tried to tap into it to improve their software and help customers operate faster and more efficiently. Kathy Baxter, principal architect for the ethical AI practice at Salesforce, will be joining us at TechCrunch Sessions: SaaS on October 27th to talk about the impact of AI on SaaS.

Baxter, who has more than 20 years of experience as a software architect, joined Salesforce in 2017 after more than a decade at Google in a similar role. We’re going to tap into her expertise on a panel discussing AI’s growing role in software.

Salesforce was one of the earlier SaaS adherents to AI, announcing its artificial intelligence tooling, which the company dubbed Einstein, in 2016. While the positioning makes it sound like a product, it’s actually much more than a single entity. It’s a platform component, which the various pieces of the Salesforce platform can tap into to take advantage of various types of AI to help improve the user experience.

That could involve feeding information to customer service reps on Service Cloud to make the call move along more efficiently, helping salespeople find the customers most likely to close a deal soon in the Sales Cloud or helping marketing understand the optimal time to send an email in the Marketing Cloud.

The company began building out its AI tooling early on with the help of 175 data scientists and has been expanding on that initial idea since. Other companies, both startups and established companies like SAP, Oracle and Microsoft, have continued to build AI into their platforms as Salesforce has. Today, many SaaS companies have some underlying AI built into their service.

Baxter will join us to discuss the role of AI in software today and how that helps improve the operations of the service itself, and what the implications are of using AI in your software service as it becomes a mainstream part of the SaaS development process.

In addition to our discussion with Baxter, the conference will also include Databricks’ Ali Ghodsi, UiPath’s Daniel Dines and Puppet’s Abby Kearns, as well as investors Casey Aylward and Sarah Guo, among others. We hope you’ll join us. It’s going to be a stimulating day.

Buy your pass now to save up to $100, and use CrunchMatch to make expanding your empire quick, easy and efficient. We can’t wait to see you in October!

Is your company interested in sponsoring or exhibiting at TC Sessions: SaaS 2021? Contact our sponsorship sales team by filling out this form.

AI and data analytics company Databricks today announced the launch of SQL Analytics, a new service that makes it easier for data analysts to run their standard SQL queries directly on data lakes. And with that, enterprises can now easily connect their business intelligence tools like Tableau and Microsoft’s Power BI to these data repositories as well.

SQL Analytics will be available in public preview on November 18.

In many ways, SQL Analytics is the product Databricks has long been looking to build and that brings its concept of a “lake house” to life. It combines the performance of a data warehouse, where you store data after it has already been transformed and cleaned, with a data lake, where you store all of your data in its raw form. The data in the data lake, a concept that Databricks’ co-founder and CEO Ali Ghodsi has long championed, is typically only transformed when it gets used. That makes data lakes cheaper, but also a bit harder to handle for users.

Image Credits: Databricks

“We’ve been saying Unified Data Analytics, which means unify the data with the analytics. So data processing and analytics, those two should be merged. But no one picked that up,” Ghodsi told me. But “lake house” caught on as a term.

“Databricks has always offered data science, machine learning. We’ve talked about that for years. And with Spark, we provide the data processing capability. You can do [extract, transform, load]. That has always been possible. SQL Analytics enables you to now do the data warehousing workloads directly, and concretely, the business intelligence and reporting workloads, directly on the data lake.”

The general idea here is that with just one copy of the data, you can enable both traditional data analyst use cases (think BI) and the data science workloads (think AI) Databricks was already known for. Ideally, that makes both use cases cheaper and simpler.

The service sits on top of an optimized version of Databricks’ open-source Delta Lake storage layer to enable the service to quickly complete queries. In addition, Delta Lake also provides auto-scaling endpoints to keep the query latency consistent, even under high loads.

While data analysts can query these data sets directly, using standard SQL, the company also built a set of connectors to BI tools. Its BI partners include Tableau, Qlik, Looker and Thoughtspot, as well as ingest partners like Fivetran, Fishtown Analytics, Talend and Matillion.

Image Credits: Databricks

“Now more than ever, organizations need a data strategy that enables speed and agility to be adaptable,” said Francois Ajenstat, chief product officer at Tableau. “As organizations are rapidly moving their data to the cloud, we’re seeing growing interest in doing analytics on the data lake. The introduction of SQL Analytics delivers an entirely new experience for customers to tap into insights from massive volumes of data with the performance, reliability and scale they need.”

In a demo, Ghodsi showed me what the new SQL Analytics workspace looks like. It’s essentially a stripped-down version of the standard code-heavy experience with which Databricks users are familiar. Unsurprisingly, SQL Analytics provides a more graphical experience that focuses more on visualizations and not Python code.

While there are already some data analysts on the Databricks platform, this obviously opens up a large new market for the company — something that would surely bolster its plans for an IPO next year.