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Today is announcing that it has raised $250 million in a Series E funding round led by , a firm which has led three of the firm鈥檚 prior rounds, inclusive of this one. Prior investors , , and participated in the deal. Two new investors, hedge fund and , joined in as well.

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The deal shows that VC interest in open source software continues apace. Just a couple weeks ago, Apache Kafka provider from at an over $2.5 billion valuation, post-money.

Databricks is now valued at $2.75 billion, post-money, according to a spokesperson for the company. This valuation lines up with targets disclosed through Delaware state regulatory filings made by the company in mid-January 2018, at the time.

This round effectively doubles Databricks鈥檚 total capital raised to date; before this round, the company said it had raised $248.5 million .1

The company didn鈥檛 disclose specific figures related to its business, but according to a statement provided to Crunchbase News the company hit $100 million in annual recurring revenue in 2018 and experienced 鈥渁pproximately 3x year-over-year growth in subscription revenue during the last quarter of 2018.鈥� Assuming the company is generating over $100 million in ARR today, its shares were valued at between 15 and 25x revenues.

The company says that over 2,000 organizations around the world use Databricks鈥檚 software in their data analytics, data science, and machine learning workflows. 鈥淲hat鈥檚 driving this incredible growth is the market鈥檚 massive appetite for Unified Analytics,鈥� said cofounder and CEO in a statement. 鈥淥rganizations need to achieve success with their AI initiatives and this requires a Unified Analytics Platform that bridges the divide between big data and machine learning.鈥�

The Unified Analytics Platform Ghodsi refers to is built atop a technology he helped co-create a decade ago. Now developed as an open-source project under the aegis of the , Spark (formally ) was developed out of the now-closed (standing for 鈥淎lgorithms Machines People鈥�) at . The original paper was published in 2010.

Crunchbase News spoke with , who currently serves as chair of the computer science department at the , to learn a little more about Spark. Franklin co-founded the AMPLab and was its director at Berkeley . He sits on several big data companies鈥� technical advisory boards, including Databricks.

鈥淪park is a platform for doing scalable data analytics and machine learning. It’s known for being very flexible, very fast, and one of its salient features is that it offers a bunch of different interfaces to interact with and operate on data,鈥� he said. In part because of the diverse set of disciplines represented by researchers at AMPLab, the framework鈥檚 scope expanded to include ways to perform SQL-style analytics queries, ingest data from streams, manipulate graph data (like social networks), and train machine learning models.

鈥淭he real reason [Spark] took off though, was because it was a faster Hadoop,鈥� Franklin said. Hadoop was the first open-source implementation of , a distributed, parallelized data processing model originally developed internally at Google. (As an aside, Hadoop鈥檚 development is also facilitated by the Apache Software Foundation. The for-profit company that supports Hadoop, , went public in October 2018.) Spark could work with data that was already loaded into the Hadoop File System (HDFS), which aided in demonstrating performance improvements eked out by the framework, prompting many to switch to Spark.

Performance may have netted Spark an initial following among the big data and analytics crowd, but the ecosystem and interoperability is what continues to drive broader adoption of Spark today. As open source software, anyone with an internet connection and a system that meets the minimum specifications can freely download and run Apache Spark on their own machines.

However, for enterprise clients that want a more full-service offering, Databricks developed a proprietary runtime鈥攖he aforementioned Unified Analytics Platform鈥攖hat is even more efficient and offers more features than the open source package. (On its website, to Apache Spark鈥檚.) Databricks offers different service tiers through partnerships with Amazon Web Services (AWS) and Microsoft鈥檚 Azure cloud compute platform.

A spokesperson for Databricks told Crunchbase News the company is 鈥渇ully committed鈥� to maintaining an open source development model for Apache Spark. 鈥淭ogether with the Spark community, Databricks continues to contribute heavily to the Apache Spark project, through both development and community evangelism,鈥� they added.

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