Databricks, the software developer of big data analytics and artificial intelligence models has raised $1 billion in its Series G funding round. The transaction brings the firm's value to $28 billion post-money valuations.
According to a statement, this financing round was conducted by new investor Franklin Templeton. It also involves the participation of the Canada Pension Plan Investment Board, Amazon Web Services (AWS), Fidelity Management & Research, Salesforce Ventures, and Alphabet's CapitalG venture arm. Alkeon Capital Management, Microsoft, Tiger Global Management, Andreessen Horowitz, Coatue Management, and the previous investors of Databricks, have also joined this round.
Headquartered in San Francisco, Databricks was formed in the year 2013 by the original developers of Apache Spark. Over 5,000 multinational corporations, including Comcast, H&M, Condé Nast, and over 40 per cent of the Fortune 500 depend on the centralized data engineering, machine learning and analytics framework of Databricks.
The estimated annual revenue of Databrick is $232.6M per year and $145,000 per employee. Furthermore, the company’s total funding is $497 million. The Databricks software supports cleaning up data for exploring data visualization applications without bothering about its configuration and upgradation.
Databricks has created its brand with a sequence of four open-source products with a chief data lakehouse technology named Delta Lake. The purpose of the data lake house is to combine cloud data lake technology with a data warehouse's advantages.
According to Databricks, this new funding will benefit in assisting its go-to-market activities and develop the data lakehouse technology. Forecasters see the Databricks financing will help to intensify the value that cloud data lakes and the lakehouse idea offer. The Amalgam Insights chief and CEO, Hyoun Park, also commented that he views the $1 billion funding round as a clear statement of support in the strategy of Databricks. The software company has previously raised $250 million and $400 million in the February and October funding rounds. Those rounds were emphasized on implementing the Delta Lake, Unified Analytics framework, and performance optimization with the open-source MLFlow software to conduct machine learning experiments and release models into operation.