Cities and municipalities face significant challenges in implementing an efficient municipal parking management system due to a lack of human and IT infrastructural resources to conduct data evaluations and build effective systems, as well as the lack of standards and APIs for efficient data sharing. Scattered and unstructured data in various formats and qualities hinder its integration with other open data sources. To address these challenges, we developed Park-an, a cloud-based service. It enables public administrators to upload, analyze, and visualize parking data in combination with other public datasets, such as OpenStreetMap (OSM). The prototype facilitates the identification of parking pressure through indicators and offers interactive visualization tools to enable better planning and decision-making. This paper outlines the software architecture of Park-an, which is based on microservices and is cloud-native, ensuring robustness and scalability. The primary feature of Park-an is the calculation of parking pressure using various indicators, such as the ratio of building area to parking area and geometric distances of points of interest (POIs) to parking spaces. The paper presents a demonstration of Park-an’s functionalities for urban parking planning and optimization based on three German cities.