Elasticsearch is a well-known NoSQL database to store and process large amounts of data, including geospatial types like points and polygons. There is a broad ecosystem of tools to help to ingest data into Elasticsearch, including some usual geospatial suspects like GDAL. Finally, Kibana is the goto solution for visualizing and making sense of all the data stored in a cluster of Elasticsearch nodes. In this talk, we will explore two archetypical use cases: on the one hand, archiving and processing information from moving or static events and, on the other, working with large amounts of data associated with a small set of asset locations. Examples of the first scenario would be tracking vehicles or databases of urban events like crime or inspection locations, while the second could be weather data archives or IoT systems. We will see how to ingest and enrich geospatial data into your cluster with these two use cases. Then we will explore some of Elasticsearch's geospatial capabilities. Finally, we will see how Kibana applications like Maps, Lens, Canvas, or Dashboards can help you visualizing and understanding your data. Finally, we want to take the opportunity to show to the community some of the latest developments made by the Kibana Maps team, like our new alerting capabilities or the support for vector tiles in Kibana and Elasticsearch.