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Overview for Lyft

Apache Beam meetup 7 at Datatonic: Beam at Lyft + datalake using Beam + schemas

See how Lyft and Datatonic are using Apache Flink, Apache beam and python in stream processing, machine learning and analytics.


Python Streaming Pipelines With Beam On Flink

Thomas Weise a Software Engineer, Streaming Platform at Lyft shares with you vital information on gains attained using Beam technology. You will learn how Lyft has contributed towards the realization of the cross-language support and enabled processing pipelines written in Python to run on Apache Flink. 


Dynamic Pricing of Lyft Rides Using Streaming

Learn how Lyft is using ML models and streaming infrastructure for low latency, reliability and scalability in pricing their rides. Amar Pai a software engineer at Lyft takes you through how they use Beam for streaming and lessons they have learned so far. 


Bootstrapping State In Apache Flink

In this discussion, Gregory Fee an engineer, Streaming Platform at Lyft talks about alternatives to bootstrap programs in Flink. Some alternatives rely on technologies exogenous to the stream program, such as enhancements to the pub/sub layer, that are more generally applicable to other stream compute engines. Other alternatives include enhancements to Flink source implementations. Lyft is exploring another alternative using orchestration of multiple Flink programs. The talk will cover why Lyft pursued this alternative and future directions to further enhance bootstrapping support in Flink.


Lyft's analytics pipeline: From Redshift to Apache Hive and Presto

Shenghu Yang explains how Lyft’s data pipeline has evolved over the years to serve its ever-growing analytics use cases, migrating from the world’s largest AWS Redshift clusters to Apache Hive and Presto for solving scalability and concurrency hard limits.