Data lakehouses are modern data architectures designed to support the integrated management and analysis of large volumes of heterogeneous, business-oriented data on distributed platforms. When such data include spatial information, a key question is how to semantically integrate it with other sources — an operation referred to as geo-enrichment — thereby creating new opportunities for more effective and insightful data analysis. 

Yet, the notion of geo-enrichment has received limited attention in the academic literature and is often associated with commercial information services. In this paper, we present our vision and discuss key challenges, particularly those related to defining a data exploration environment that provides geo-enrichment operators and tools for both discovering relevant data sources and interacting with geo-enrichable data. 

Our discussion is grounded in a use case involving the integration of spatial datasets provided by Eurostat—the statistical office of the European Union (EU)—within Apache Sedona on a Spark cluster, as adopted in the context of the EU-funded GRINS project.