Lyft is a multidimensional marketplace. Not only does it require a buyer and a seller (a.k.a. a driver and a passenger); it also needs them to be interested at the same time and at the same place. Reacting to events with traditional methodologies is challenging, especially when timely reaction is required to balance the market condition. Thus, data science tools for machine learning and a process that allows for faster deployment is of growing importance to the business.
Rakesh Kumar and Thomas Weise discuss how Lyft uses its streaming platform to run dynamic pricing algorithms, allowing the company to be fair to drivers (by say, raising rates when there’s a lot of demand) and to passengers (by offering to return 10 minutes later for a cheaper rate). To accomplish this, the system consumes a massive amount of events from different sources.
Rakesh Kumar is a software engineer on the pricing team at Lyft. He started his career as an embedded software engineer for mobile devices; later he moved to server-side engineer to tackle bigger challenges in distributed systems. His interests include machine learning and streaming systems.
Thomas Weise is a software engineer for the streaming platform at Lyft. He’s also a PMC member for the Apache Apex and Apache Beam projects and has contributed to several more projects within the ASF ecosystem. Thomas is a frequent speaker at international big data conferences and the author of Learning Apache Apex.
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