Benchling today announced Direct Data Share, a new capability integrated with Snowflake, the AI Data Cloud company. Direct Data Share keeps scientific data continuously connected between Benchling and a company’s Snowflake environment.
With AI a part of everyday R&D, biotech companies need scientific data to be current, governed, and usable across the tools scientists rely on. Direct Data Share enables seamless, bi-directional data flow between Benchling and Snowflake. Experimental data in Benchling lands in Snowflake as ready-to-query tables refreshed in near real time. While instrument, assay, computational, and third-party data in Snowflake can flow back into Benchling alongside the experiments and context that give it meaning.
For IT and data teams, Direct Data Share is a simple one-time setup in Benchling. Connect a Snowflake account and Benchling data begins landing as ready-to-query tables, refreshed in minutes. There is no custom export pipeline to build or maintain as the underlying scientific data model evolves. Benchling keeps the shared data current. Admins control who can initiate syncs and manage connections, so compliance and security requirements stay intact.
The result is a more continuous R&D data loop: experiments generate data; that data can then feed downstream analytics and models; and the resulting insights can return to the scientists making the next decision.
“AI is changing what’s possible in R&D, but it requires a complete and consistent structure to reach its potential,” said Benchling Head of Product, Derek Halliday. “The time it takes to sync data from one source to another can’t be the choke point for effective decision making. With Direct Data Share, Benchling keeps scientists’ data current seamlessly, helping our scientists and IT teams meet the demands of the lab of the future.”
As Jesse Cugliotta, Head of Healthcare and Life Sciences at Snowflake notes “Scientific data becomes far more valuable when teams can put it to work alongside the rest of the organization’s data. Connecting Benchling and Snowflake allows biopharma companies to make governed experimental data continuously available for analytics, models, and AI, ensuring scientists have the broader context required to drive better decision-making.”
Direct Data Share expands on the momentum of Benchling’s AI Connectors, launched earlier this year. The two capabilities support the different ways R&D teams work with scientific data. AI Connectors are designed for interactive work, for example when an agent or AI application retrieves relevant scientific context as needed. Direct Data Share is designed for continuous, large-scale analysis: millions of rows can be queried directly, joined with data from other systems, and used repeatedly across analytics and model workflows.
