It would help to understand the current issues that you have with this approach? I used a similar approach (not with Flink, but a similar big data technology) some years ago
> Am 04.03.2019 um 11:32 schrieb Wouter Zorgdrager <w.d.zorgdra...@tudelft.nl>: > > Hi all, > > I'm working on a setup to use Apache Flink in an assignment for a Big Data > (bachelor) university course and I'm interested in your view on this. To > sketch the situation: > - > 200 students follow this course > - students have to write some (simple) Flink applications using the > DataStream API; the focus is on writing the transformation code > - students need to write Scala code > - we provide a dataset and a template (Scala class) with function signatures > and detailed description per application. > e.g.: def assignment_one(input: DataStream[Event]): DataStream[(String, Int)] > = ??? > - we provide some setup code like parsing of data and setting up the > streaming environment > - assignments need to be auto-graded, based on correct results > > In last years course edition we approached this by a custom Docker container. > This container first compiled the students code, run all the Flink > applications against a different dataset and then verified the output against > our solutions. This was turned into a grade and reported back to the student. > Although this was a working approach, I think we can do better. > > I'm wondering if any of you have experience with using Apache Flink in a > university course (or have seen this somewhere) as well as assessing Flink > code. > > Thanks a lot! > > Kind regards, > Wouter Zorgdrager
