codecentric mittendrin
Meetups, Stammtische, Hackathons, User Groups: Die codecentric ist weit mehr als die Summe ihrer Mitarbeiter und Projekte.
Software Craftsmanship Rhein-Main 18.11.2017 09:00 Uhr
Global Day of Coderetreat 2017
On November 18th we will join the worldwide Software Craftsmanship community for the Global Day of Coderetreat. Die Teilnahmegebühr wird zurückerstattet, wenn ihr zum Coderetreat erscheint. Mit den 15,- EUR wollen wir nur sicherstellen, dass auch alle kommen, die sich ...
Computer Vision Karlsruhe 07.11.2017 18:00 Uhr
Einführung in Computer Vision (OpenCV, Python, Basics)
Die Ferien sind vorbei, die Projekte laufen wieder – jetzt wollen wir mit unserem ersten Meetup „Computer Vision Karlsruhe“ loslegen. Unser Ziel ist es eine aktive Community zu etablieren und gemeinsam an interessanten Problemen zu arbeiten. Unser Meetup soll einen ...
Search Technology Meetup Hamburg 06.11.2017 18:30 Uhr
Machine Learning
The tenth edition of the Search Technology Meetup Hamburg will be hosted at codecentric Hamburg Office: Come and join us for food, drinks and another edition of awesome inspiring talks. We thank Elasticsearch for sponsoring foods, codecentric for location + ...
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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