Presented By
O’Reilly + Cloudera
Make Data Work
29 April–2 May 2019
London, UK

Schedule: Transportation and Logistics sessions

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14:5515:35 Wednesday, 1 May 2019
Data Engineering and Architecture
Location: Capital Suite 8/9
Mark Grover (Lyft), Deepak Tiwari (Lyft)
Lyft’s data platform is at the heart of Lyft’s business. Decisions all the way from pricing, to ETA, to business operations rely on Lyft’s data platform. Moreover, it powers the enormous scale and speed at which Lyft operates. In this talk, Mark Grover walks through various choices Lyft has made in the development and sustenance of the data platform and why along with what lies ahead in future. Read more.
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14:5515:35 Wednesday, 1 May 2019
Law and Ethics, Strata Business Summit
Location: Capital Suite 10/11
Our experience with building the Business Intelligence platform has been nothing short of extraordinary. The proposal contains details about how Uber thought about building it's Business Intelligence platform. In this talk, I’ll narrate the journey of deciding on how we took a platform approach rather than adding features in a piecemeal fashion. Read more.
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16:3517:15 Wednesday, 1 May 2019
Data Science, Machine Learning & AI
Location: Capital Suite 14
Divya Choudhary (GOJEK)
Data scientists around the globe would agree that addresses are the most unorganised textual data. Structuring addresses has almost led to a new stream of NLP itself. Who would've imagined that address text data can be used to develop one of the coolest product feature of finding the most precise pick up/drop-off locations for e-commerce, logistics, food delivery or ride/car services companies! Read more.
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17:2518:05 Wednesday, 1 May 2019
Felix Cheung (Uber)
Did you know that your Uber rides are powered by Apache Spark? Join Felix Cheung to learn how Uber is building its data platform with Apache Spark at enormous scale and discover the unique challenges the company faced and overcame. Read more.
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11:1511:55 Thursday, 2 May 2019
Brandy Freitas (Pitney Bowes)
Data science is an approachable field given the right framing. Often, though, practitioners and executives are describing opportunities using completely different languages. In this session, Harvard Biophysicist-turned-Data Scientist, Brandy Freitas, will work with participants to develop context and vocabulary around data science topics to help build a culture of data within their organization. Read more.
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11:1511:55 Thursday, 2 May 2019
Thomas Weise (Lyft)
Fast data and stream processing are essential for making Lyft rides a good experience for passengers and drivers. Our systems need to track and react to event streams in real-time, to update locations, compute routes and estimates, balance prices and more. The streaming platform at Lyft powers these use cases with development frameworks and deployment stack that are based on Apache Flink and Beam. Read more.
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12:0512:45 Thursday, 2 May 2019
Data Engineering and Architecture
Location: Capital Suite 8/9
Václav Surovec (Deutsche Telekom IT), Gabor Kotalik (Deutsche Telekom AG)
The knowledge of location and travel patterns of customers is important for many companies. One of them is a German telco service operator T-Mobile Czech Republic. Commercial Roaming project using Cloudera Hadoop helped the company to better analyze the behavior of its customers from 10 countries, in a very secure way, to be able to provide better predictions and visualizations for the management. Read more.
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14:0514:45 Thursday, 2 May 2019
Data Engineering and Architecture
Location: Capital Suite 8/9
Willem Pienaar (GO-JEK), Zhi Ling Chen (GO-JEK)
Features are key to driving impact with AI at all scales. By democratizing the creation, discovery, and access of features through a unified platform, organizations are able to dramatically accelerate innovation and time to market. Find out how GO-JEK, Indonesia's first billion-dollar startup, built a feature platform to unlock insights in AI, and the lessons they learned along the way. Read more.
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14:0514:45 Thursday, 2 May 2019
Data Engineering and Architecture
Location: Capital Suite 10/11
Ravi Suhag (Go Jek)
At GO-JEK, we build products that help millions of Indonesians commute, shop, eat and pay, daily. The Data team is responsible to create resilient and scalable data infrastructure across all of GO-JEK’s 18+ products. This involves building distributed big data infrastructure, real-time analytics and visualization pipelines for billions of data points per day. Read more.
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14:5515:35 Thursday, 2 May 2019
Data Science, Machine Learning & AI
Location: Capital Suite 15/16
Christopher Hooi (Land Transport Authority of Singapore)
The Fusion Analytics for Public Transport Event Response (FASTER) system provides a real-time advanced analytics solution for early warning of potential train incidents. Using novel fusion analytics of multiple data sources, FASTER harnesses the use of engineering and commuter-centric IoT data sources to activate contingency plans at the earliest possible time and reduce impact to commuters. Read more.
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16:3517:15 Thursday, 2 May 2019
Data Science, Machine Learning & AI
Location: Capital Suite 15/16
GRDF helps bring natural gas to nearly 11 million customers everyday. In partnership with GRDF, Dataiku worked to optimise the manual process of qualifying addresses to visit and ultimately save GRDF time and money. This solution was the culmination of a year-long adventure in the land of maintenance experts, legacy IT systems and agile development. Read more.