Databricks on Thursday raised $500 million in new financing, raising the data lakehouse pioneer's total valuation to $43 billion.
The series I funding round follows Databricks' series H round of $1.6 billion in August 2021 and series G round of $1 billion in February 2021.
T. Rowe Price Associates led the latest round, with other investors including -- among others -- Andreessen Horowitz, Fidelity Management & Research Company and Franklin Templeton, all of which were previous Databricks investors. New investors include Capital One Ventures, the Ontario Teachers' Pension Plan and AI hardware/software vendor Nvidia.
Based in San Francisco, Databricks helped develop the data lakehouse concept and architecture when the company was founded in 2013.
Data lakehouses combine the structured data storage capabilities of data warehouses with the unstructured and semistructured data storage capabilities of data lakes, enabling users to store all types of data in a single location and making it easier to combine disparate types of data for more complete analysis.
Combining different data types is key for generative AI, given that AI model performance is dependent on the data used to train and feed the models. The more complete -- and better quality -- the data is, the more accurate the model will be.
Data lakehouses, therefore, are now viewed as perhaps the best option for generative AI.
In addition to Databricks, data lakehouse vendors include Dremio and Cloudera. Tech giants Amazon, Google and Microsoft also offer data lakehouse capabilities.
While Databricks has continued to attract venture capital investment over the past couple of years, funding for most data management and analytics vendors has slowed significantly since the start of 2022, when inflation spiked and fears of a recession arose.
Tech stock values plummeted at the start of 2022, which forced vendors such as Qlik and ThoughtSpot that had been planning initial public offerings to put plans on hold. Even now, closing in on two years later, conditions for IPOs remain largely unfavorable.
Meanwhile, just as public market investment conditions have been difficult for data vendors, so have private market investment conditions.
However, established companies with strong financials are somewhat of an exception, noted Matt Aslett, an analyst at Ventana Research. For example, Pyramid Analytics raised $120 million in May 2022, and Denodo raised $336 million on Sept. 13.
Matt AslettAnalyst, Ventana Research
"Although venture capital funding might be harder to raise than in recent years, there is still funding available for established vendors with proven traction and opportunities for growth," Aslett said. "Databricks is squarely in that category with its Lakehouse Platform for data management, data engineering, data streaming and AI."
According to Databricks, its revenue is up 50% year over year, and it now has more than 10,000 customers worldwide.
Databricks did not reveal its plans for the new funding. In June, however, the vendor acquired MosaicML for $1.3 billion to enable customers to develop their own generative AI models. Before that, in March, Databricks released Dolly, its own large language model.
It's logical, therefore, to expect that Databricks will use its series I funding to continue investing in generative AI, according to Doug Henschen, an analyst at Constellation Research.
"Strong funding helped Databricks acquire MosaicML earlier this year, but we're in the infancy of AI, and particularly generative AI, so this additional funding round can't hurt when it comes to additional organic development and possible acquisitions," he said.
Henschen added that Databricks acquired significant generative AI expertise and capabilities with MosaicML, but that company was still in its early stages. Therefore, development work remains to bring MosaicML's potential to fruition.
"The MosaicML acquisition was all about helping customers to develop their own custom models, but ... Databricks will need to scale up its capabilities to serve its large and growing customer base," he said.
One specific way Databricks could add generative AI throughout its suite of tools is by infusing the industry-specific versions of its platforms with domain-specific generative AI.
Like Snowflake, Databricks has developed a series of industry-specific versions of its platform to better serve certain customers. Databricks began with its Lakehouse for Retail in January 2022, and other industry-specific versions of the platform include financial services, healthcare and manufacturing.
"An avenue for organic development might be bolstering AI use cases across the company's many industry-specific clouds," Henschen said. "Organizations are tantalized by the possibilities for AI, but they also want to see concrete use cases that are relevant to their specific industry and business."
Aslett likewise said he expects Databricks to use the new funding to continue investing in generative AI capabilities.
But there are other areas as well where the vendor would be wise to invest, he continued. Though most data management and analytics vendors are focused on adding generative AI capabilities, the foundation underlying generative AI -- data management to ensure data quality -- remains critical.
Therefore, according to Aslett, Databricks would be wise to continue adding and investing in capabilities such as its Unity Catalog, a data catalog it first introduced in June 2022.
"Potential focus areas for investment include enhancing the company's data catalog and data governance capabilities with data lineage, data quality and data observability," Aslett said.
Now that Databricks has reached its series I funding round, Aslett suggested an IPO could be in the vendor's plans.
Snowflake executed the largest IPO ever by a tech company in September 2020. But that was at a time when market conditions were favorable for IPOs.
Henschen, meanwhile, echoed the assessment that Databricks would be wise to focus on investing in building up its foundational capabilities in addition to adding more generative AI functionality.
"All things AI are in the white-hot spotlight, but I'd like to see Databricks continue to build out the more conventional data warehousing and analytics capabilities that were the focus of the company's attention before generative AI reset agendas for most every software company," he said. "Data is the foundation of AI, so sound and robust data management, metadata management and data governance is essential."
Eric Avidon is a senior news writer for TechTarget Editorial and a journalist with more than 25 years of experience. He covers analytics and data management.