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Starburst acquires data lake analytics accelerator Varada to challenge Snowflake

·2 min read

Starburst, the well-funded data warehouse analytics service and data query engine based on the open source Trino project, today announced that it has acquired Varada, a Tel Aviv-based startup that focuses on data lake analytics. Using Varada's proprietary indexing technology, enterprises can easily operationalize their data lakes, a capability that neatly extends Starburst's query engine. This, Starburst argues, will allow it to better challenge the likes of Snowflake and Databricks in the growing data analytics market.

"With the addition of Varada’s indexing technology, we can help data teams better serve the business, providing the right data right now. This powerful combination couldn’t come at a better time when an uncertain economy is forcing companies to reevaluate their budgets, as business demands only increase," explains Starburst CEO Justin Borgman in today's announcement.

Likewise, Varada co-founder and VP of R&D Roman Vainbrand notes that the combined companies will be able to "deliver the best performance and cost benefits in the analytics query market, enabling organizations to accelerate the time-to-insight, while optimizing infrastructure operations and investments."

Varada's engineering and product leadership teams will join Starburst and the two companies expect to be able to launch an integration between their products in the next month, with general availability by fall 2022. The Starburst team believes that the integrated product will allow its users to significantly reduce their cloud compute costs and improve their query response times by up to 7x.

The two companies did not disclose the price of the acquisition. Varada last raised a $12 million Series A round led by MizMaa Ventures in 2020. The company had some leadership changes over the years, with its co-founder and former CEO and CTO David Krakov leaving the company in late 2021, for example. We didn't hear much from Varada since its last funding round, but this is definitely an example of the data analytics market starting to consolidate as the underlying technologies mature, usage patterns crystalize and a small number of companies start to emerge as early winners.