4th
June 2018
Data Engineer Workshop
Date: June 4th, 2018
Location: Criteo Paris – Auditorium
Speakers: Criteo Data Reliability Engineering team
4th
June 2018
Workshop Agenda
5th
June 2018
Main conference
There are lots of conferences these days and almost all of them promise new and magical insights that will surely revolutionize the way you work. This is not one of them. NABD’s main conference is first and foremost about engineers solving problems and sharing those resolutions with others–and we encourage our speakers to share the bruises they’ve accumulated along the way, because even the best of us have seen some pretty spectacular failures.
So, come join us for a day of sharing, learning, fun and yes, perhaps some group therapy!
Neville Li
Samantha Hansen
Fallon Chen
Barclay Osborn
Benjamin Pence
Quentin François
Gil Chamiel
Guillaume Bort
Matthieu Blumberg
Benoit Hanotte
Justin Coffey
Maxime Kestemont
Francois Jehl
Charles Labroy
Hakan Jakobsson
5th
June 2018
Main Conference Agenda
Event Location
Criteo Paris
32 rue Blanche
75009 Paris France
Previous years
Get in Touch
Paris Office
32 Rue Blanche,
75009 Paris, France
Telephone: +33 1 40 40 22 90
Fax: +33 1 40 40 22 30
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Palo Alto, CA 94301 US
Telephone: +1 650 322 6260
Fax: +1 650 322 6159
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Telephone: +1 646 565 4133
Grenoble Office
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38130 Echirolles
Telephone: +33 4 85 19 00 54
A full day of workshops dedicated to understand the basics and the challenges of big data by the Criteo Data Reliability Engineering team. What is data engineering all about? How can we leverage Scala, Python and SQL to do data transformations? Which language is right in which circumstances? What about ad-hoc data exploration and the data engineering development cycle? How can we manage truly interactive reporting on top of TBs of data? And finally, what are the best practices for monitoring and organizing all of a company’s datasets?
These are core questions a budding data engineer should ask herself and once armed with responses she’ll be better prepared to not only be a more efficient data engineer, but also confront the FUD and general misinformation surrounding data engineering and big data systems in general.