UPDATED 12:46 EDT / SEPTEMBER 09 2021

BIG DATA

Databricks announces closing of its $1.6B funding round

Databricks Inc. today announced the final closing of the $1.6 billion funding round it first disclosed last month and revealed a notable new detail: All three of the industry’s top cloud providers participated in the investment. 

The $1.6 billion round values Databricks at a hefty $38 billion.

It’s not often that Amazon Web Services Inc., Microsoft Corp. and Google LLC parent Alphabet Inc. back the same startup. In the case of Google, the investment was made through the search giant’s private equity fund. Databricks partners with all three companies to make its platform available on their respective public clouds.

“We are delighted to once again include our most strategic partners in this latest round of funding, as it validates our vision for an open and unified approach to data and AI on any cloud,” Databricks co-founder and Chief Executive Officer Ali Ghodsi (pictured) said in a statement today. “As we jointly make more organizations successful in their move to the cloud and accelerate adoption of the lakehouse architecture, we’re excited to see these partnerships – and the ecosystems formed around them – continue to grow for decades to come.”

Databricks didn’t disclose how much AWS, Microsoft and CapitalG invested in the round. The startup previously shared that the $1.6 billion investment had been led by Morgan Stanley & Co LLC’s Counterpoint Global Fund with participation from more than a dozen others. The list includes The Bank of New York Mellon Corp., BlackRock Inc., Fidelity Management & Research Co., Franklin Templeton Financial Services Corp. and T. Rowe Price Group Inc., as well as leading venture capital firms such as Andreessen Horowitz.

San Francisco-based Databricks provides a cloud-native data platform that enables enterprises to build a data lakehouse. Lakehouse is the company’s term for a type of data management environment that combines the features of two earlier, widely used product categories: data warehouses and data lakes.

Data warehouses have been used mainly to process structured records such as customer purchase logs. A data lake, in contrast, is optimized for unstructured records such as artificial intelligence training datasets. A lakehouse can accommodate both structured and unstructured information.

Databricks’ lakehouse architecture adds in other key features as well. Data lake solutions have lacked some of the capabilities found in data warehouses, such as support for the ACID reliability standard, which ensures the integrity of a company’s information even if technical issues occur. Support for the standard is considered an essential feature for mission-critical enterprise workloads. Databricks-powered lakehouses provide ACID support and other features from data warehouses, while offering the flexibility that has made data lakes popular for storing unstructured information.

The benefit for enterprises is increased operational efficiency. Instead of having to operate separate data warehouses and data lakes, a company can standardize its applications on a lakehouse, which simplifies maintenance because there are fewer moving pieces to manage. A second advantage of the reduced complexity is that common analytics tasks can be streamlined

Databricks’ technology also lends itself to implementing a data mesh, a popular data management architecture. The architecture allows different business units at a company to independently process their information, a big departure from the usual approach of centralizing all the data in one system. Databricks allows companies to implement a data mesh by creating a separate lakehouse for each business unit.

Databricks’ technology is used by more than 5,000 organizations worldwide, including over 40% of the Fortune 500. The startup has raised about $3.5 billion from investors since launch. 

Photo: SiliconANGLE

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