Consumer-facing real-time processing poses a number of challenges to protect against fraudulent transactions and other risks. The streaming platform at Lyft seeks to support this with an architecture that brings together a data science-friendly programming environment with a deployment stack for the reliability, scalability, and other SLA requirements of a mission-critical stream processing system.
Thomas Weise and Mark Grover explain how Lyft uses its streaming platform to detect and respond to anomalous events. Reacting to such events with traditional development methodologies is challenging, especially where low-latency SLAs for instant user feedback are critically important. Enablement of data science tools for machine learning and a process that allows for fast and predictable deployment is of growing importance.
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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.
Mark Grover is a product manager at Lyft. Mark’s a committer on Apache Bigtop, a committer and PPMC member on Apache Spot (incubating), and a committer and PMC member on Apache Sentry. He’s also contributed to a number of open source projects, including Apache Hadoop, Apache Hive, Apache Sqoop, and Apache Flume. He’s a coauthor of Hadoop Application Architectures and wrote a section in Programming Hive. Mark is a sought-after speaker on topics related to big data. He occasionally blogs on topics related to technology.
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Hi all,
We are super excited to see you all real soon! You won’t want to miss this.
The slides are posted at go.lyft.com/streaming-at-lyft