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Lyft is a ridesharing company with petabytes of data. Within Lyft, many different employees can use those data sets to build useful applications.
A business analyst creates a dashboard to see how driver satisfaction is changing over time. An economist studies the pricing data to ensure that Lyft’s prices are competitive. A data scientist creates a report of how the speed of a ride correlates with 5 star ratings. A machine learning engineer trains a model to detect fraud on the platform.
All of these use cases make sense–and in each of them, the employee at Lyft needs to find the necessary data sets within the company to build their application. Amundsen is a tool for finding and discovering data sets within the company.
Tao Feng and Mark Grover are engineers at Lyft and join the show to talk about the problem of data discovery and the tools they have built at Lyft.
142 episodes available. A new episode about every 6 days averaging 57 mins duration .