codecentric mittendrin
Meetups, Stammtische, Hackathons, User Groups: Die codecentric ist weit mehr als die Summe ihrer Mitarbeiter und Projekte.
PottNet – Essener Microsoft Developer Group 21.11.2018 19:00 Uhr
3D-Visualisierung eines smarten Gebäudes mit BabylonJS
Eine vernetzte Firmenzentrale ist nur dann sinnvoll, wenn die Bedienung intuitiv ist: Im Rahmen des SmartBuildingBoards zeigen wir, wie zahlreiche IoT-Geräte, von der Lampe über den Temperatursensor bis hin zum smarten Kicker, über eine 3D-Benutzeroberfläche kontrolliert werden können. Dabei beschränken ...
Munich Software Craft Community 17.11.2018 9:00 - 17:00 Uhr
Global Day of Coderetreat 2018
On Saturday, November 17th, 2018, thousands of developers all over the globe will honor their craft, share knowledge, and practice by “playing our instruments behind the scenes” to learn from each other. All events around the world and coordinated at ...
Java User Group Hamburg 25.10.2018 19:00 Uhr
Spring Boot Admin
In dieser Hands-on Session lernst Du, wie Du Deine Spring-Boot-Anwendungen mit dem Spring Boot Admin steuern und überwachen kannst. Wir werden uns das einfache Einrichten des Spring Boot Admins anschauen und welche unterschiedlichen Optionen es gibt. Um zu beweisen, dass ...
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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