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Databricks on Aug. 31 revealed a $1.6 billion Series H funding round, bringing the data management and AI vendor's valuation to $38 billion.
The new financing round is the second time this year that Databricks has raised a large sum from investors, opting to finance development of its data lakehouse technology with private capital for now instead of going public, as had been widely expected. In February, the vendor raised $1 billion in a Series G round.
Databricks still expects to file for an initial public offering at some point, however, it said in a statement provided to TechTarget.
The sharp pace of the San Francisco-based vendor's growth comes as other data management vendors -- notably Snowflake with its $3.6 billion IPO in September 2020 -- have also expanded quickly and dramatically and reflects strong enterprise demand for the Databricks data lakehouse and other cloud data and AI platforms.
Databricks noted that as of now its plan is to create the conditions for long-term growth.
"This round is creating even better [options] in terms of how and when Databricks chooses to go public," the vendor said. "This funding doesn't change anything in terms of our outlook on going public -- it's still very much part of our plan."
Dave Menninger, senior vice president and research director at Ventana Research, said he sees the new funding as further validation that the data market that Databricks participates in is extremely dynamic.
"Clearly the investors think Databricks is a big winner," Menninger said. "If you look at the valuations, they are betting that Databricks will be another Snowflake."
Snowflake's IPO was the largest software firm public offering in tech history.
At the time Snowflake went public, it was valued at about $33 billion. The vendor started as a cloud data warehouse specialist and has since expanded to include a suite of cloud data tools for organizations to analyze and use data.
Databricks is active in growing markets
Databricks has its roots in the open source Apache Spark SQL query engine project. The vendor has also expanded its offering into AI in recent years. In May, Databricks introduced a series of enhanced AI capabilities including automated machine learning.
"Spark is hot, AI is hot, big data continues to be hot and cloud is hot," Menninger said. "Databricks is involved in all of these."
Menninger noted that Databricks has also established effective partnerships for distribution and awareness about its products, with an ecosystem of complementary technology partners.
Dave MenningerSenior vice president and research director, Ventana Research
Growing the data lakehouse vision at Databricks
In recent years a key focus area for Databricks has been the data lakehouse. The basic concept behind the lakehouse is to merge the best features of a data warehouse and a data lake into a data architecture.
Databricks' core technology for the data lakehouse is the open source Delta Lake project, which has been part of the Linux Foundation since 2019.
In 2020, Databricks added a SQL analytics service powered by Delta Lake to its Unified Data Analytics Platform, which also included enhanced data visualization technology that came with the acquisition of Redash in Jun 2020.
Also in May, the vendor unveiled a new data sharing service known as Delta Sharing that aims to make it easier for users to share data across the lakehouse, as well as data catalog and data governance capabilities packaged as Unity Catalog.