Databricks acquires AI data platform Einblick
Data and AI company Databricks has arranged to acquire AI-focused data platform Einblick to expand its capabilities in the field.
Einblick has developed state-of-the-art techniques for translating natural language questions into the code, charts and models needed to generate data insights. The company was founded by researchers from MIT and Brown University.
Databricks CEO and co-founder Ali Ghodsi said the company is thrilled to welcome Einblick co-founders Emanuel Zgraggen and Philipp Eichmann to the Databricks team.
“Like us, they believe that organisations need to deeply leverage data and AI to be successful in this new age,” he said. “We look forward to incorporating Einblick’s innovative, AI-native approach directly into the Databricks platform, enabling organisations to democratise data intelligence and create the next generation of data and AI applications with quality, speed and agility.”
The acquisition is one of a string of purchases Databricks has completed in the past year, including real-time data replication technology provider Arcion in October, generative AI platform MosaicML and AI storage systems startup Rubicon in June, and AI-centric data governance platform Okera in May.
Databricks completed a Series I financing round in September which has valued the company at US$43 billion ($65.2 billion).
The funding round was led by T. Rowe Price Associates and supported by existing Databricks investors including Andreessen Horowitz, Baillie Gifford, ClearBridge Investments. New investors including NVIDIA and the Ontario Teachers’ Pension Plan also took part.
Lumify Group acquires Wizard Corporate Training
Lumify Group has expanded its range of professional development courses through the acquisition...
Mimecast buys collaboration security platform Aware
Mimecast aims to enhance its human risk management capabilities through the acquisition of AI...
Tech sector scales back economic growth forecasts
A growing proportion of Australian ICT sector respondents are predicting flat to small growth in...