To support the roll-out of fibre broadband Internet in Germany, Deutsche Telekom has set itself the goal of connecting more than 2.5 million households per year to FTTH (Fibre to the Home). However, planning and approval processes have been very complex and time-consuming in the past due to high communication overhead between stakeholders, missing automation, and lack of information about planning areas. Telekom addresses this problem by collecting large amounts of geospatial data (3D point clouds and 360°panorama images), which can be used to automatically find suitable routes for fibre optic lines, to determine possible locations for distribution cabinets, as well as to build a 3D visualization helping to create detailed plans and to present them to decision makers. This speeds up planning tremendously, but processing this data and creating the visualization in a short time requires automation. In this systems paper, we present a data processing platform that we have built and operated together with Telekom over the course of the last six and a half years. The platform makes use of the cloud to manage Big Data in a scalable and elastic manner. It builds upon research results from us, specifically a scientific workflow management system to automate processing, as well as Fibre3D, a web-based tool that planners can use to display the processed data and to perform fine-planning. Besides the technological aspects, this paper also describes a practical use case that shows how the platform and Fibre3D help Telekom speed up the planning and approval process. We also summarize lessons learned and give recommendations for the design of systems similar to ours. With the presented technology, Telekom has been able to already connect more than 8 million households to FTTH and expects to even improve on this in the future. We consider our collaboration, therefore, an example of how well knowledge and technology transfer between research and industry can work, and, at the same time, what impact it can have on society. • Software platform to speed up the fibre roll-out in Germany. • Cloud-based, Big Data processing pipeline for 360°panorama imagery and 3D point clouds. • Web-based 3D visualization used for interactive planning. • More than 8 million households have already been connected to fibre Internet. • Lessons learned and general design recommendations.

Summary Function as a service (FaaS) promises low operating costs, reduced complexity, and good application performance. However, it is still an open question how to migrate monolithic applications to FaaS. In this paper, we present a guideline for software designers to split monolithic applications into smaller functions that can be executed in a FaaS environment. This enables independent scaling of individual parts of the application. Our approach consists of three steps: We first identify the main tasks (and their subtasks) of the application to split. Then, we define the program flow to be able to tell which application tasks can be converted to functions and how they interact with each other. In the final step, we specify actual functions and possibly merge those that are too small and which would produce too much communication overhead or maintenance effort. Compared to existing work, our approach applies to applications of any size and results in functions that are small enough—but not too small—for efficient execution in a FaaS environment. We evaluate the usefulness of our approach by applying it to a real‐world application for the storage of geospatial data. We describe the experiences made and finish the paper with a discussion, conclusions, and ideas for future work.

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