Home / SmartTech / Databricks exceeded its $ 350 million execution rate in the third quarter, up from $ 200 million a year ago – TechCrunch

Databricks exceeded its $ 350 million execution rate in the third quarter, up from $ 200 million a year ago – TechCrunch



The data analytics firm quickly scaled to embark on an obvious IPO path

The stock market regularly Covers companies as they approach and hit the $ 100 million mark. Our goal in tracking startups growing at scale is to track down future IPO candidates and better understand the late stage funding market.

Today we’re digging into a company that’s a bit bigger. That said, Databricks, a data analytics company that was valued at around $ 6.2 billion in its Series F in October 201

9 when it raised $ 400 million.


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The former startup hit an execution rate of around $ 350 million by the end of Q3 2020, after $ 200 million in revenue in Q3 2019, which brought it to a rapid pace of growth for a former startup of its size.

To better gauge the company’s performance, I phoned its CEO, Ali Ghodsi, hoping to better understand how Databricks has grown as much as it has over the past few years. Ghodsi took over the helm in 2016 after serving as the company’s vice president of engineering. He is also a co-founder.

Databricks is an obvious IPO candidate, but also a company with broad options for the private market due to its revenue growth and attractive profitability. Today let’s talk about Databricks’ growth story, how it changed its sales process, and what the unicorn faces more than six times.

What does Databricks do?

What does Databricks actually do? Usually I would be content with waving the data analysis and calling it a day. However, chatting with Ghodsi cleared the matter up so let me help.

Let’s say a company has a lot of data about its machines and wants to know when different parts will fail. Or maybe a company wants to find patterns in some economic data. How do you find this information?

Ghodsi assumes you will need three things: First, data engineering, or getting customer data into the right forms so that you can actually use it. Second, the data science, which Ghodsi describes as “the machine learning algorithms, the predictive algorithms you need”. Third, companies “increasingly” want data warehousing and some “deep analytics,” he added.


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