Presented By O’Reilly and Cloudera
Make Data Work
September 11, 2018: Training & Tutorials
September 12–13, 2018: Keynotes & Sessions
New York, NY
Mark Grover

Mark Grover
Product Manager, Lyft

@mark_grover

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.

Sessions

11:20am–12:00pm Thursday, 09/13/2018
Near-real-time anomaly detection at Lyft
Location: 1E 07/08 Level: Beginner
Secondary topics:  Temporal data and time-series analytics, Transportation and Logistics
Thomas Weise (Lyft), Mark Grover (Lyft)
Average rating: **...
(2.50, 2 ratings)
Thomas Weise and Mark Grover explain how Lyft uses its streaming platform to detect and respond to anomalous events, using data science tools for machine learning and a process that allows for fast and predictable deployment. Read more.
2:00pm–2:40pm Thursday, 09/13/2018
Big data at speed
Location: 1A 06/07 Level: Intermediate
Secondary topics:  Transportation and Logistics
Ted Malaska (Capital One), Mark Grover (Lyft)
Many details go into building a big data system for speed, from determining a respectable latency until data access and where to store the data to solving multiregion problems—or even knowing just what data you have and where stream processing fits in. Mark Grover and Ted Malaska share challenges, best practices, and lessons learned doing big data processing and analytics at scale and at speed. Read more.