Databricks crossed $350M run rate in Q3, up from $200M one year ago

The data analytics company scaled rapidly to put itself on an obvious IPO path

The Exchange regularly covers companies as they approach and crest the $100 million revenue mark. Our goal in tracking startups growing at scale is to scout future IPO candidates and better understand the late-stage financing market.

Today we’re digging into a company that is a little bit bigger than that. Namely Databricks, a data analytics company that was most recently valued at around $6.2 billion in its October, 2019 Series F when it raised $400 million.


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The former startup reached a run rate of around $350 million at the end of Q3 2020, up from $200 million in revenue in Q3 2019, putting it on a rapid growth pace for a former startup of its size.

To better dig into the company’s performance, I got on the phone with its CEO, Ali Ghodsi, hoping to better understand how Databricks has managed to grow as much as it has in recent years. Ghodsi took over as CEO in 2016 after serving as the company’s VP of engineering. He’s also a co-founder.

Databricks is an obvious IPO candidate, but it’s also a company with broad private-market options, given its revenue expansion and attractive economics. Today, let’s talk about Databricks’ growth history, how it changed its sales process, and what’s ahead for the unicorn more than six times over.

What does Databricks do?

What does Databricks actually do? Normally I’d be content to wave my hands at data analytics and call it a day. Chatting with Ghodsi, however, clarified the matter, so let me help.

Let’s say that a company has a lot of data on its machinery and wants to know when different pieces are going to fail. Or, perhaps a company wants to find patterns in some economic data. How do they find that information?

Ghodsi reckons you need three things: First, data engineering, or getting customer data “massaged into the right forms so that you can actually start using it.” Second, data science, which Ghodsi describes as “the machine learning algorithms, the predictive algorithms that you need to have.” And third, on top, companies “more and more” also want data warehousing and some “basic analytics,” he added.

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