This panel features Starburst Regional Manager, Brian Luisi, and Starburst CTOs, Dain Sundstrom and David Philips. They cover how Starburst and Trino assist in the responsibilities of data scientists, including collecting data, interacting with data, and making predictive models. They also discuss what data access means for data scientists, Trino use cases, and best practices for data scientists.
Data scientists need a data engine that can interact with all of their data and is able to quickly sift through it, which allows for easier profiling and exploration. Trino and Starburst allows data scientists to do this by providing a single point of access to large amounts of data. David describes Trino as “fast and distributed, so if you need to process data, you can process it way faster than you could on a Python script.” Trino’s SQL-based MPP query engine provides great value to scientists looking to expedite their processing through its ability to analyze large volumes of data, fast.