Presented By O'Reilly and Cloudera
Make Data Work
September 26–27, 2016: Training
September 27–29, 2016: Tutorials & Conference
New York, NY
Mark Grover

Mark Grover
Product Manager, Lyft


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.


1:30pm–5:00pm Tuesday, 09/27/2016
Hadoop use cases
Location: Hall 1C Level: Intermediate
Jonathan Seidman (Cloudera), Mark Grover (Lyft), Ted Malaska (Capital One)
Average rating: ****.
(4.08, 13 ratings)
Jonathan Seidman, Gwen Shapira, Mark Grover, and Ted Malaska demonstrate how to architect a modern, real-time big data platform and explain how to leverage components like Kafka, Impala, Kudu, Spark Streaming, and Spark SQL with Hadoop to enable new forms of data processing and analytics such as real-time ETL, change data capture, and machine learning. Read more.
1:15pm–1:55pm Wednesday, 09/28/2016
Spark & beyond
Location: Hall 1B Level: Advanced
Ted Malaska (Capital One), Mark Grover (Lyft)
Average rating: ***..
(3.92, 12 ratings)
Ted Malaska and Mark Grover cover the top five things that prevent Spark developers from getting the most out of their Spark clusters. When these issues are addressed, it is not uncommon to see the same job running 10x or 100x faster with the same clusters and the same data, using just a different approach. Read more.
1:15pm–1:55pm Thursday, 09/29/2016
Location: 1 C03
Mark Grover (Lyft), Jonathan Seidman (Cloudera), Ted Malaska (Capital One)
Average rating: *****
(5.00, 1 rating)
Mark Grover, Jonathan Seidman, and Ted Malaska, the authors of Hadoop Application Architectures, participate in an open Q&A session on considerations and recommendations for the architecture and design of applications using Hadoop. Come with questions about your use case and its big data architecture or just listen in on the conversation. Read more.