Recent & Upcoming Talks

The data orchestrator is at the heart of the data pipelines. We start by exploring how a modern data orchestrator drastically eases the development of pipelines. Then we will see how govanance can be conducted efficiently in a MDS-based setup.

Using large scale telecommunication data for mobility modeling and infrastructure maintenance

Good quality network connectivity is ever more important. For hybrid fiber coaxial (HFC) networks, searching for upstream \emph{high noise} in the past was cumbersome and time-consuming. Even with machine learning due to the heterogeneity of the network and its topological structure, the task remains challenging. We present the automation of a simple business rule (largest change of a specific value) and compare its performance with state-of-the-art machine-learning methods and conclude that the precision@1 can be improved by 2.3 times. As it is best when a fault does not occur in the first place, we secondly evaluate multiple approaches to forecast network faults, which would allow performing predictive maintenance on the network.

The fragmented modern data stack has emerged as the unbundling of Airflow. Various tools operate in silos. Dagster as a next-generation data orchestrator allows you to clearly see the data dependencies of the individual pipelines on your data factory floor.

Mobility monitoring in Austria during the COVID NPI

recent history of data processing.

Introduction to processing geospatial data from classical small data applications to big data workloads

Intro duction to processing large quantities of data in R

Moving a draft ML application to a real world production environment can be hard. This talk outlines several tools and strategies to do …