Presented By O'Reilly and Cloudera
Make Data Work
22–23 May 2017: Training
23–25 May 2017: Tutorials & Conference
London, UK

Schedule: Ask Me Anything sessions

11:1511:55 Thursday, 25 May 2017
Location: Capital Suite 7
Mark Grover (Lyft), Jonathan Seidman (Cloudera), Ted Malaska (Capital One)
Average rating: *****
(5.00, 2 ratings)
Mark Grover, Ted Malaska, and Jonathan Seidman, the authors of Hadoop Application Architectures, share 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.
12:0512:45 Thursday, 25 May 2017
Location: Capital Suite 7
John Akred (Silicon Valley Data Science), Stephen O'Sullivan (Data Whisperers), Scott Kurth (Silicon Valley Data Science)
Average rating: ***..
(3.33, 3 ratings)
John Akred, Scott Kurth, and Stephen O'Sullivan field a wide range of detailed questions about developing a modern data strategy, architecting a data platform, and best practices for (and the evolving role of) the CDO. Even if you don’t have a specific question, join in to hear what others are asking. Read more.
14:0514:45 Thursday, 25 May 2017
Location: Capital Suite 7
Tim Berglund (Confluent)
Join Tim Berglund to discuss topics from his tutorial, Real-time data pipelines with Apache Kafka, or ask any other questions you have. Read more.
14:5515:35 Thursday, 25 May 2017
Location: Capital Suite 7
Angie Ma (Faculty), Scott Stevenson (Faculty)
Average rating: ****.
(4.00, 1 rating)
Angie Ma and Scott Stevenson share their experience and lessons learned from having worked on over 160 commercial data science projects with over 120 organizations from different sectors and industries. Read more.
16:3517:15 Thursday, 25 May 2017
Location: Capital Suite 7
Vartika Singh (Cloudera), Jayant Shekhar (Sparkflows Inc.), Jeffrey Shmain (Cloudera)
Average rating: *****
(5.00, 1 rating)
Join Vartika Singh, Jayant Shekha, and Jeffrey Shmain to ask questions about their tutorial, Unraveling data with Spark using machine learning, or anything else Spark related. Read more.