codecentric mittendrin
Meetups, Stammtische, Hackathons, User Groups: Die codecentric ist weit mehr als die Summe ihrer Mitarbeiter und Projekte.
IoT Hessen 20.09.2018 18:00 Uhr
IoT Connectivity: NarrowBand IoT – Game Changer for the IoT?
NarrowBand IoT (NB-IoT) is a low-power wide area network (LPWAN) technology that many big players in the field are currently exploring and including into their portfolio. Let’s take a closer look at the technology and see how it can help ...
Java User Group Bonn 04.09.2018 18:30 Uhr
Spring Boot Admin & Chaos Engineering
Bei den Treffen der Java User Group Bonn wird es immer einen oder auch mehrere Vorträge von bekannten Speakern und Rookies geben. Für das leibliche Wohl ist gesorgt. 18:30 – Einlass und Begrüßung 19:00 – Talk #1: Spring Boot Admin ...
Agiler Stammtisch Frankfurt 29.08.2018 18:45 Uhr
Agil und kreativ – UX Designprozesse im Zeitalter von Scrum & DevOps
Während sich die Art, wie wir Software entwickeln und liefern, in den letzten 20 Jahren radikal verändert hat, haben die Prozesse für Design und Spezifikation von Benutzerschnittstellen häufig nicht Schritt gehalten. Die Folge sind unnötige Mehraufwände und Produkte, die trotz ...
Stream Processing and War Stories
We just renamed the group from SMACK to Fast Data and would like to host the second meetup. We will attempt to host this every two months, so please do not hesitate if you have any cool talks! See you around! Agenda for the evening:
SMACK stack from the trenches (Achim Nierbeck)
In this talk I will give an overview about the SMACK – Stack (Streaming, Mesos, Akka, Cassandra, Kafka) and tell about the experiences made with this stack and how to fine tune it for massive data. For a better understanding on how a SMACK stack might work, a demonstration showing how to stream live bus data from Los Angeles with Spark and Flink is shown.
Stream Processing Comparison (Matthias Niehoff)
In this talk I will give an overview on various concepts used in data stream processing. Most of them are used for solving problems in the field of time, focussing on processing time compared to event time. The techniques shown include the Dataflow API as it was introduced by Google and the concepts of stream and table duality. But I will also come up with other problems like data lookup and deployment of streaming applications and various strategies on solving these problems. In the end I will give a brief outline on the implementation status of those strategies in the popular streaming frameworks Apache Spark Streaming, Apache Flink and Kafka Streams.
As always Drinks and Pizza are on us! Please care, Please share!
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