Presented By O'Reilly and Cloudera
December 5-6, 2016: Training
December 6–8, 2016: Tutorials & Conference
Singapore
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

9:00am–12:30pm Tuesday, December 6, 2016
Hadoop use cases
Location: 310/311 Level: Intermediate
Mark Grover (Lyft), Ted Malaska (Capital One), Jonathan Seidman (Cloudera)
Average rating: ****.
(4.75, 4 ratings)
Mark Grover, Ted Malaska, and Jonathan Seidman explain how to architect a modern, real-time big data platform leveraging recent advancements in the open source software world and discuss how to use components like Kafka, Impala, Kudu, Spark Streaming, and Spark SQL with Hadoop to enable new forms of data processing and analytics. Read more.
11:15am–11:55am Wednesday, December 7, 2016
Spark & beyond
Location: Summit 2 Level: Intermediate
Ted Malaska (Capital One), Mark Grover (Lyft)
Average rating: ****.
(4.12, 8 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.
2:35pm–3:15pm Wednesday, December 7, 2016
Ask Me Anything
Location: 310/311
Mark Grover (Lyft), Jonathan Seidman (Cloudera), Ted Malaska (Capital One)
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.