DBTB INT Andy Petrella
summary
A platform for interoperable, scalable genomics and health data that moves teams from ad hoc analysis to production-ready pipelines by pushing curated datasets instead of manual discovery. It uses graph analysis and NLP to match data to tasks and runs on Spark and HDFS for enterprise scale. The claim is measurable: it reduces data-prep and discovery overhead for data scientists, and the toolkit is already running in research labs and enterprise pilots, integrating with existing runtimes.
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