Geo-enrichment in a Data Lakehouse: Exploring Challenges and Opportunities

Data lakehouses are modern data architectures designed to sup- port 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 re- ferred 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 liter- ature and is often associated with commercial information services. In this paper, we present our vision and discuss key challenges, par- ticularly those related to defining a data exploration environment that provides geo-enrichment operators and tools for both discover- ing 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.