Presented By O’Reilly and Cloudera
Make Data Work
21–22 May 2018: Training
22–24 May 2018: Tutorials & Conference
London, UK

Schedule: Transportation and Logistics sessions

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11:1511:55 Wednesday, 23 May 2018
Data science and machine learning, Data-driven business management
Location: Capital Suite 10/11 Level: Beginner
Average rating: *****
(5.00, 6 ratings)
Because in-house data science teams work with a range of business functions, traditional data science processes are often too abstract to cope with the complexity of these environments. Alberto Rey Villaverde and Grigorios Mingas share case studies from easyJet that highlight some unpredictable hurdles related to requirements, data, infrastructure, and deployment and explain how they solved them. Read more.
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12:0512:45 Wednesday, 23 May 2018
Baolong Mao (JD.com), Yiran Wu (JD.com), Yupeng Fu (Alluxio)
Mao Baolong, Yiran Wu, and Yupeng Fu explain how JD.com uses Alluxio to provide support for ad hoc and real-time stream computing, using Alluxio-compatible HDFS URLs and Alluxio as a pluggable optimization component. To give just one example, one framework, JDPresto, has seen a 10x performance improvement on average. Read more.
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14:0514:45 Wednesday, 23 May 2018
Data engineering and architecture
Location: S11B Level: Intermediate
Carsten Herbe (Audi Business Innovation GmbH), Matthias Graunitz (Audi AG)
Average rating: *****
(5.00, 1 rating)
Carsten Herbe and Matthias Graunitz detail Audi's journey from a Hadoop proof of concept to a multitenant enterprise platform, sharing lessons learned, the decisions Audi made, and how a number of use cases are implemented using the platform. Read more.
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14:5515:35 Wednesday, 23 May 2018
Data engineering and architecture
Location: S11B Level: Beginner
Timo Graen (Volkswagen AG ), Robert Neumann (Ultra Tendency)
Map-matching applications exist in almost every telematics use case and are therefore crucial to all car manufacturers. Timo Graen and Robert Neumann detail the architecture behind Volkswagen Commercial Vehicle’s Altus-based map-matching application and lead a live demo featuring a map matching job in Altus. Read more.
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12:0512:45 Thursday, 24 May 2018
Mark Grover (Lyft), Ted Malaska (Capital One)
Average rating: *****
(5.00, 4 ratings)
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.