Our First Kafka Meetup with 2 amazing speakers form Confluent

Hi everyone!

Please click here to register if you are interested in attending the meetup! The reason for this is that we are also promoting the meetup outside meetup.com and also we need to keep track of the people of the people who are actually attending so we are aware of room capacity and catering. Thank you so much! (http://go.confluent.io/2017.05.30-EMEA-Germany-WaldorfMeetup_Register.html)

Join us for our first Apache Kafka meetup on May 30th from 6:00pm - 8:00pm, hosted by SAP Exchange Media (SAP XM) in Walldorf. The address is Dietmar-Hopp-Allee 22, 69190 Walldorf, Building WDF05 (Schulungszentrum/Training Center), Room S3 (Basement level). The agenda and speaker information can be found below. See you there!

For questions, or if you can't get in, call Ralph Debusmann (0151-43820180).

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Agenda:
6:00pm: Doors open
6:00pm - 6:30pm: Networking, Pizza and Drinks
6:30pm - 7:00pm: Presentation #1: Highly Scalable Machine Learning and Deep Learning Microservices with Apache Kafka Streams, Kai Waehner, Confluent
7:00pm - 7:30pm: Presentation #2: Four Real-World Streaming Architectures, Tim Berglund, Confluent
7:30pm - 8:00pm: Additional Q&A and Networking

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First Talk

Speaker:
Kai Waehner

Bio:
Kai Waehner works as Technology Evangelist at Confluent. Kai’s main area of expertise lies within the fields of Big Data Analytics, Machine Learning, Integration, Microservices, Internet of Things, Stream Processing and Blockchain. He is regular speaker at international conferences such as JavaOne, O’Reilly Software Architecture or ApacheCon, writes articles for professional journals, and shares his experiences with new technologies on his blog ( http://www.kai-waehner.de/blog) . Contact and references: kontakt@kai-waehner.de / @KaiWaehner / www.kai-waehner.de (http://www.kai-waehner.de/)

Title:
Highly Scalable Machine Learning and Deep Learning Microservices with Apache Kafka Streams

Abstract:
Big Data and Machine Learning are key for innovation in many industries today. The first part of this session explains how to build analytic models with R, Python or Scala leveraging open source machine learning / deep learning frameworks like Apache Spark, TensorFlow or H2O.ai. The second part discusses the deployment of these built analytic models to your own applications or microservices; leveraging the Apache Kafka cluster and Kafka Streams instead of setting up a new, complex stream processing cluster. The session focuses on live demos and teaches lessons learned for executing analytic models in a highly scalable and performant way. The last part explains how Apache Kafka can help to move from a manual build and deployment of analytic models to continuous online model improvement in real time.

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Second Talk

Speaker:
Tim Berglund
Bio:
Tim is a teacher, author, and technology leader with Confluent, where he serves as the Senior Director of Developer Experience. He can frequently be found at speaking at conferences in the United States and all over the world. He is the co-presenter of various O’Reilly training videos on topics ranging from Git to Distributed Systems, and is the author of Gradle Beyond the Basics. He tweets as @tlberglund, blogs very occasionally at http://timberglund.com (http://timberglund.com/), is the co-host of the http://devrelrad.io (http://devrelrad.io/) podcast, and lives in Littleton, CO, USA with the wife of his youth and their youngest child, the other two having mostly grown up.

Title:
Four Real-World Streaming Architectures

Abstract:
Streaming systems have begun to move past trivial implementations of distributed message queues into more comprehensive architectures that model changed data as a first-class citizen, rather than try to hide changes behind update-in-place databases. This is not an uncommon way of modeling behavior inside a single application, but is a new paradigm in the distributed systems thinking of most engineers.

In this talk, we’ll consider four real-world streaming architectures based on Apache Kafka. We’ll look at the particular problems posed by the four different use cases, and how the systems solved them, with reference to features of Kafka, Kafka Streaming, and where appropriate, open-source extensions like Kafka Connect and schema features.

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Special thanks to SAP Exchange Media (SAP XM) who are hosting us for this event.

Don't forget to join our Community Slack Team (https://slackpass.io/confluentcommunity)!

If you would like to speak or host our next event please let us know! community@confluent.io

NOTE: We are unable to cater for any attendees under the age of 18. Please do not sign up for this event if you are under 18.