After decades of an overwhelming GIS methodological approach to geographical data, recent data deluge led the way toward a new direction in geographical data analysis. It’s true for “big data” as well as for the massive and heterogeneous datasets published as part of the open data movement. This renewal of geodata analysis can be referred as “Geographic Data Science” (Arribas-Bel and Reades, 2018 ; Singleton and Arribas-Bel, 2019), while others prefer to use an older term like “GeoComputation” (originally forged by Openshaw et See, 2003 and redefined by Lovelace et al., 2019). In those recent publications, authors point out that these new approaches could be relevant to “infuse new developments in the area of infrastructure to support Exploratory Spatial Data Analysis (ESDA)” (Singleton and Arribas-Bel, op. cit., p. 8). Simultaneously, the technical tools dedicated to data (spatial or not )analysis and visualisation keep developing and become more and more user-friendly, requiring less and less technical and computer science background. This makes it easier nowadays to grasp for human geographers. Such tools can be stand-alone software promoting new ways of representing and analysing data, such as CARTO or Deck.gl. It can also rely on new “software ecosystems” built around computer programming languages such as javascript, Python or R. Such ecosystems together enable the pre-processing, the statistical and spatial analysis and the (geo)visualisation of large spatial data by relying on a galaxy of libraries, packages and external software bindings. This vastly used ecosystem of data analysis tools builds upon classical methods that can be translated into these software. The example of PySal (Rey and Anselin, 2010) Python library that ported a part of the methods of GeoDa (Anselin et al., 2006) illustrates this aspect These software ecosystems are mostly agnostic to the data source: they are able to connect to most of the modern sources of data, from basic text files to distributed High Performance Computing nodes, passing through traditional relational DBMS. While new tools dedicated to data analysis emerge, more and more data storage and organisation solutions appears frequently. The recent “column-oriented DBMS” , especially, allow the querying of large quantities of data in an extremely fast way, and are now usable even on standard computers, without requiring hours of pre-processing. We defend that the current resurgence of geographic data analysis – based on data that are together more massive, more heterogeneous but also more accessible -- can be greatly eased by the use of renewed ESDA Such ESDA could rely on data stored on modern DBMS that would be queried from data analysis computer languages and libraries. The presentation will focus on diverse ways to leverage the use of such new technologies, enabling a renewed vision of ESDA applied to larger sources of data. This communication is strongly rooted in the intersection of technics and methodology. The demonstration will be based on a case study, focusing on a new dataset opened by french government. This dataset gathers all the real-estate transactions in France for the past 5 years We intend to show a way of process ing the dataset analysis in an integrated R workflow, from traditional gui-based analysis to an ad-hoc exploration web-application. Anselin, Luc, Ibnu Syabri, and Youngihn Kho. 2006. ‘GeoDa: An Introduction to Spatial Data Analysis’. Geographical Analysis 38 (1): 5–22. https://doi.org/10.1111/j.0016-7363.2005.00671.x. Arribas‐Bel, Dani, and Jon Reades. 2018. ‘Geography and Computers: Past, Present, and Future’. Geography Compass 0 (0): e12403. https://doi.org/10/gd56wf. García González, Juan Antonio. 2019. ‘Visual and Spatial Thinking in the Neogeography Age’. In Geospatial Challenges in the 21st Century, edited by K. Koutsopoulos, R. de Miguel González, and K. Donert, 369–83. Key Challenges in Geography. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-04750-4_19. Lovelace, Robin, Jakub Nowosad, and Jannes Muenchow. 2019. Geocomputation with R. The R Series. Chapman and Hall/CRC. https://geocompr.robinlovelace.net/. Openshaw, Stan, and Linda M See. 2003. Geocomputation. Independence, USA: CRC Press. Rey, Sergio J., and Luc Anselin. 2010. ‘PySAL: A Python Library of Spatial Analytical Methods’. In Handbook of Applied Spatial Analysis: Software Tools, Methods and Applications, edited by M. Fischer and A. Getis, 175–93. Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-03647-7_11. Singleton, Alex, and Daniel Arribas‐Bel. 2019. ‘Geographic Data Science’. Geographical Analysis 0 (0). https://doi.org/10.1111/gean.12194.