Past Meetup

Integrating Kafka by PAYBACK and Confluent

This Meetup is past

76 people went

Location image of event venue

Details

Join us for our next Munich Apache Kafka® meetup on September 12th from 6:00pm, hosted by PAYBACK. The agenda, venue and speaker information can be found below. See you there!

-----

Agenda:
6:00pm: Doors open
6:00pm - 6:30pm: Pizza, Drinks and Networking
6:30pm - 7:15pm: Unleashing Apache Kafka and TensorFlow in the Cloud, Kai Waehner - Confluent
7:15pm - 7:45pm - Microservice Choreography with Apache Kafka, Robert Pemsel, PAYBACK
7:45pm – 8:15pm - StreamSets Data Collector closing the gap between classic RDBMS and BigData world, Hazhir Hajian - PAYBACK
8:15pm – 10:00pm - Additional Q&A & Networking

------

Speaker 1:
Kai Waehner, Confluent

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 / Deep Learning, Messaging, Integration, Microservices, Stream Processing, Internet of Things 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 (www.kai-waehner.de/blog). Contact and references: [masked] / @KaiWaehner / www.kai-waehner.de

Title:
Unleashing Apache Kafka and TensorFlow in the Cloud

Abstract:

How can you leverage the flexibility and extreme scale in the public cloud combined with your Apache Kafka ecosystem to build scalable, mission-critical machine learning infrastructures, which span multiple public clouds or bridge your on-premise data centre to cloud?
This talk will discuss and demo how you can leverage machine learning technologies such as TensorFlow with your Kafka deployments in public cloud to build a scalable, mission-critical machine learning infrastructure for data ingestion and processing, and model training, deployment and monitoring.
The discussed architecture includes capabilities like scalable data preprocessing for training and predictions, combination of different Deep Learning frameworks, data replication between data centres, intelligent real time microservices running on Kubernetes, and local deployment of analytic models for offline predictions.

----------

Speaker 2:
Robert Pemsel

Bio:
Robert is a Software Developer for the PAYBACK International Platform.
His main focus is the transition into a microservice based system.
Therefore he loves to use Apache Kafka for decoupling microservices.

Title:
Microservice Choreography with Apache Kafka

Abstract:
Complex business processed in a system often span multiple Microservices. In this case it is challenging to guarantee data consistency if involved components do not support XA transactions. Compensating transactions could be used, but the underlying data model must support them.
Another option is to decouple Microservices by using Choreography with the help of Apache Kafka being used as an event streaming platform. The presentation will cover the design ideas and implications of this approach reaching from the design of producers, message format through consumer error handling in the surrounding of transaction driven systems to enable data consistency just with local transactions.

----------

Speaker 3
Hazhir Hajian

Bio:
Hazhir Hajian works as Big Data Engineer at PAYBACK implementing solutions based on Apache Hadoop, Apache Kafka. He has strong Linux background and his main interest is to experiment with new technologies around BigData world.

Title:
StreamSets Data Collector closing the gap between classic RDBMS and BigData world

Abstract:
At PAYBACK, as a data driven company, we constantly try to close the gap between classic RDBMS and BigData technologies. This talk is about PoC on how StreamSets Data Collector can use Oracle Change Data Capture feature to stream data from Oracle to Kafka. This is a feature by StreamSets Data Collector and it will be discussed in details. Other StreamSets use cases will be discussed briefly as well.