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First Streamline Hackathon in Munich

Preceding the 3rd Joint International BBDC and ScaDS Summer School for Big Data we hold a big data hackathon from August 19th-20th in Munich. The hackathon aim is to attract a diverse crowd of people from around the world from academia and companies and in particular computer science master students. The participants learn in the two days hackathon about building complex real-time data analytics pipelines based on open-source cluster-computing frameworks (i.e., Apache Flink and Apache Spark).

In the open coding session, the participants implement their ideas using the frameworks. The participants will get a short introduction and sample programs in the frameworks. The challenge to solve is to pick one or more real-time data sources, either provided by the samples or self chosen and optionally historical data sources and build real-time analytics, which improve strategic planning or decision making.

We will give prizes to the groups with best ideas, implementations, and demonstrations.

Agenda

August 19 August 20
09:00 - 10:00 Welcome & Keynote Coding Session
10:00 - 10:30 Coffee BREAK Coffee BREAK
10:30 - 12:00 Introduction to Framework APIs Coding Session
12:00 - 13:00 LUNCH LUNCH
13:00 - 14:00 Problem Definition Coffee BREAK
14:00 - 15:00 Coding Session Coding Session
15:00 - . . . Coding Session Presentation & Evaluation

Registration

The hackathon attendance is free and food and drinks will be provided.

The places for the hackathon are limited. The summer school participants have priority.

Please follow the link to register for the first Streamline Hackathon.

This is not necessary if you will register for the Summer School, or have already registred.

 

We highly recommend to attend the hackathon as students. In this case, team building up to 3 students is possible.

Hackathon Prizes

  • 1st Place

    1500 €
  • 2nd Place

    1000 €
  • 3rd Place

    500 €

This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 688191.