Data management provider Databricks Inc. has raised more than $500 million in funding at a $43 billion valuation.

T. Rowe Price Associates led the Series I round with contributions from a dozen other backers, Databricks detailed in its announcement of the investment today. Those other backers included Nvidia Corp. and Andreessen Horowitz. Capital One Ventures, Morgan Stanley and Franklin Templeton were also among the participants.

Databricks’ new $43 billion valuation is $5 billion higher than what the company was worth following its previous funding round in 2021. To date, the software maker has raised more than $4 billion in funding.

The company disclosed on occasion of its latest raise that its annual revenue run rate now exceeds $1.5 billion. The company reached that milestone after growing its sales by 50% year-over-year in the second quarter. According to Databricks, its software is now used by more than 10,000 organizations worldwide.

Databricks provides a data lakehouse that enterprises can use to store, organize and analyze large amounts of information. The platform is designed as an alternative to the data warehouses and data lakes that have historically been used for the task.

A data lake is a software platform that can store structured, semistructured and unstructured information in a cost-efficient manner. Data warehouses, meanwhile, can likewise hold structured information but don’t support other types of records as well. However, they do offer several capabilities typically not included in data lakes, including so-called ACID features that reduce the risk of errors.

Historically, analyzing information was a two-step process in the enterprise. Companies had to move records from the systems where they’re usually stored into a data lake. Then, they had to move the records a second time from the data lake to a data warehouse for analysis, which is a complicated task. Moreover, information often becomes out-of-date by the time it reaches the data warehouse. 

Databricks’ lakehouse avoids the traditional two-step analytics workflow. It does so by combining the features of a data lake and a data warehouse in a single platform. Companies can store, prepare and analyze their records in the lakehouse without moving them to an external data warehouse.

“The commitment from long-term focused strategic and financial partners reflects Databricks’ continued momentum, the rapid customer adoption of the Databricks Lakehouse, and the success customers are seeing from moving to a unified data and AI platform,” said co-founder and Chief Executive Ali Ghodsi (pictured).

Databricks’ platform is underpinned by Apache Spark, an open-source analytics engine. The software is popular in the enterprise because it can process large amounts of data quickly. Spark was developed by Ghodsi and Databricks’ other co-founders at the University of California at Berkeley before they launched the company in 2013. 

Databricks’ lakehouse combines Spark with Delta Lake, an open-source tool that provides ACID support. ACID is a reliability standard that ensures the changes a company makes to the data in its lakehouse are carried out without errors. Additionally, the standard lowers the risk that an unexpected hardware outage will cause information loss.

The third open-source technology underpinning Databricks’ lakehouse is MLflow. It’s a tool that makes it easier to build and deploy artificial intelligence models. Over the past few quarters, Databricks has significantly expanded its AI feature set, which may have factored into Nvidia’s decision to back its newly announced $500 million funding round.

“Enterprise data is a goldmine for generative AI,” said Nvidia founder and Chief Executive Officer Jensen Huang. “Databricks is doing incredible work with Nvidia technology to accelerate data processing and generative AI models.”

A few months before today’s funding announcement, Databricks spent $1.3 billion to acquire a machine learning startup called MosaicML. San Francisco-based Mosaic developed a platform for training generative AI models. Earlier, Databricks introduced a tool that eases the task of integrating neural networks into enterprise applications.

The new funding that Databricks has raised could make it easier for the company to finance new feature development initiatives in the AI market and beyond. Additionally, some of the capital could potentially be invested in acquisitions. 

Photo: SiliconANGLE