Track3: Data Craftsmanship

Solving streaming problems

in real-time

Ever wanted to dive into the tools that detect credit card fraud in real-time, make personalized video recommendations, or alert you to high CO2 levels in a room?

This workshop offers a unique opportunity to engage with real-world streaming data problems. Participants will tackle a genuine challenge posed by one of our clients: making decisions based on live air quality sensor data, including identifying faulty sensors and determining room occupancy. You will be using Python, Java, SQL, Kubernetes, Flink, and Kafka. Sample code and guidance will be provided to help you develop innovative solutions.

Learning goal

Participants will gain insight into real-time tools and tackle a genuine challenge posed by one of our clients: making decisions based on live air quality sensor data, including identifying faulty sensors and determining room occupancy.

Pre-requisites

Knowledge of Java or SQL or Python. Templates will be given in Java and SQL. Any knowledge of distributed stream processing would be beneficial. There will be easier and more difficult questions to choose from.

  • Samuel von Baussnern

    Samuel holds a B.Sc. in Neuroinformatics from the University of Zurich (UZH) and a M.Sc. in Computer Science from EPFL. Before joining D ONE, he worked as a Machine Learning Engineer, Software Engineer and Data Scientist at various Startups. Samuel has been with the team since 2021.

  • Marianna Arvaniti

    Marianna holds an M.Sc.in Computer Science from the University of Crete, Greece and a Diploma in Computer Science & Engineering from the University of Ioannina, Greece. Before joining D ONE she worked as a Research & Development Engineer at Proxitour, as well as a Research Assistant at ICS-FORTH. Marianna has been with the team since 2022.