Flink Streaming at Ericsson Research

In this 4th Stockholm Apache Flink meetup, we will have 3 talks and a hands-on workshop!

Here's the tentative schedule:

1. Welcome and intro (18:00)

2. Community Update and Apache Flink 0.10 Overview (18:10), by Paris Carbone (KTH, Apache Flink committer)

----- Break for networking and beverages -----

3. Flink @ Ericsson Research: (18:45)

• Kappa Architecture, by Ignacio Mulas Viela (Ericsson Research)

Kappa architecture and its applicability to a particular Ericsson use case where it has been applied. Concretely, detecting anomalies using telemetry/logging information of a production cloud system will be demonstrated.
The presentation will cover: an explanation of the main principles driving the Kappa architecture followed by a use case implementing it.

Speaker: Ignacio Mulas Viela, cloud analytics researcher working at Ericsson Research. His main area of research is related to the study of (data-driven) management on cloud infrastructures and analytics applications running on cloud infrastructures.

• Storm vs Flink: Hands-on experiences, by Konstantinos Vandikas (Ericsson Research)

Experiences obtained in migrating a working application written in Storm to its equivalent in Flink. Hands-on technical experience obtained from this process will be presented and shared.

Speaker: Konstantinos Vandikas, Master Researcher at Ericsson Research, currently working as a technical lead in the area of the Internet Of Things (IoT) and M2M Communications. His background is in service oriented architectures, distributed systems and programming languages. Konstantinos holds a Ph.D. in Computer Science from RWTH Aachen, Germany.

----- Break for networking and beverages -----

4. Workshop: Local tools for developing and testing streaming applications (19:45)

Explanation and workshop using some test tools that can be used in local environments when developing streaming applications. Testing the end-to-end analytics application, for example, how to use a local kafka to test serialization/deserialization of streaming events in your local computer.

-------- Finish around 9 ---------