Enterprise software tech startup Databricks raises $1.6 billion in funding; pushing its valuation from $28 billion to $38 billion (up $10 billion in just 6 months) | Tech News | Startups News

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As you may recall, we covered Databricks after Amazon, Alphabet, Salesforce, and others invested $1 billion in the cloud-native tech startup to scale and support the company’s rapid adoption of the lakehouse, which is quickly becoming the data architecture of choice for data-driven organizations around the world. The $1 billion in Series G funding valued the company at $28 billion.

Six months later, Databricks announced Tuesday it raised $1.6 billion in a Series H funding round, pushing the company’s valuation from $28 billion to $38 billion, a $10 billion in just 6 months. This Series H round was led by Morgan Stanley’s Counterpoint Global fund, alongside new investors Baillie Gifford and New York City-based ClearBridge Investments.

Existing investors including BlackRock, Andreessen Horowitz, Tiger Global Management, T. Rowe Price Associates, and Fidelity Investments also participated in the round. To date, Databricks has now raised nearly $3.6 billion in total funding since its inception eight years ago.

Databricks also announced it’s on pace to generate $1 billion or more in 2022 revenue, a 75% year over year revenue growth. The company also said its annual recurring revenue has jumped to $600 million, up from about $425 million the previous year


Founded in 2013 by Ali Ghodsi (CEO), Andy Konwinski, Ion Stoica, Matei Zaharia, Patrick Wendell, Reynold Xin, Scott Shenker, the Silicon Valley-based Databricks is a software platform that helps its customers unify their analytics across the business, data science, and data engineering. More than 5,000 organizations worldwide, including Comcast, Condé Nast, Nationwide, H&M, and over 40% of the Fortune 500, use Databricks’ unified data platform for data engineering, machine learning, and analytics.

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Databricks rose to prominence after it helped companies implement a version of Apache Spark, an alternative to the Hadoop technology for storing lots of different kinds of data in massive quantities.

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