The dynamic nature of Linked Data from IoT devices, social media, and the financial sector requires efficient mechanisms to keep SPARQL query results up to date, as traditional reevaluation methods are computationally expensive and impractical. Incremental view maintenance (IVM) offers a more efficient alternative by updating query results incrementally. However, existing engines lack support for federated querying, dynamically adding and removing sources during query execution, SPARQL Query Language support, multiple IVM techniques, and client-side execution. In this paper, we present Incremunica, an incremental query engine that addresses these gaps. Incremunica uniquely integrates multiple state-of-the-art incremental operators, allowing it to adapt to different queries and data for optimal performance. In this article, we provide 1) a requirements analysis comparing Incremunica to related work, 2) an explanation of Incremunica’s architecture and features, 3) a performance evaluation showing improvements over reevaluation, and 4) a demonstration of its benefits through a social media watch party application. Resource type: software framework License: MIT DOI: https://doi.org/10.5281/zenodo.15085224 URL: https://github.com/comunica/incremunica .