Databases
DuckDB Spatial combines the power of modern analytical databases and GIS into a single lightweight solution. With support for GeoParquet, GeoJSON, Shapefiles, and advanced Spatial SQL functions, you can analyze large geospatial datasets at lightning speed without complex database infrastructure. In this hands-on course, you’ll learn how to perform spatial analyses, combine datasets, and develop efficient geospatial data workflows
DuckDB Spatial is increasingly being used for the rapid analysis of large geospatial datasets within GIS, Geo-ICT, data engineering, and modern analytics workflows. Organizations use this technology, for example, to process GeoParquet files, perform spatial SQL analyses, and combine geodata with Python, R, and notebook environments.
The spatial extension is an add-on for DuckDB that allows spatial data to be analyzed directly with SQL. Unlike traditional spatial databases, DuckDB does not need to be installed or managed as a separate server. This makes this solution particularly suitable for GIS specialists, data analysts, researchers, and developers who want to work with geodata quickly and flexibly.
A key advantage is support for modern geodata formats such as GeoParquet, GeoJSON, Shapefile, and GeoPackage. GeoParquet, in particular, is playing an increasingly important role in GeoAI, cloud data lakes, and scalable geospatial analysis environments. With DuckDB, these files can be queried directly using Spatial SQL, without the data first needing to be loaded into a heavy database environment.
The course aligns well with modern workflows using Python, R, GeoPandas, sf, and Jupyter notebooks. This creates a practical bridge between traditional GIS analysis, spatial databases, and modern data science. Participants learn how to efficiently analyze, combine, and prepare large geospatial datasets for maps, reports, dashboards, models, and further processing.
This course is of interest to anyone working with GIS, Geo-ICT, data analysis, or data engineering who wants to handle geospatial datasets faster, more easily, and with greater flexibility. The extension combines the power of Spatial SQL with the convenience of working from files and programming environments.
Key points when working with spatial data in DuckDB:
DuckDB Spatial is an extension of DuckDB for working with spatial data. While DuckDB excels at analytical queries on tabular data, this extension adds support for geometries, spatial functions, and geodata formats. This makes the technology suitable for applications where geographic datasets need to be explored, cleaned, merged, and analyzed.
With this approach, participants can quickly turn raw geodata into actionable spatial insights. This makes DuckDB a valuable tool for professionals who want to work efficiently with large geospatial datasets without immediately setting up a complex spatial database infrastructure.
In this course, participants learn how to use spatial data for practical GIS and geodata analysis. The focus is on writing clear Spatial SQL queries, working with geometries, and performing analyses on larger geospatial datasets.
The technology is often used in combination with programming languages, GIS tools, and data science environments. In the course, participants learn how this approach fits into modern workflows with Python, R, GeoPandas, sf, and notebooks.
This course helps professionals work with modern geodata faster and more easily. Participants lay a solid foundation for applications in GIS, Geo-ICT, data engineering, GeoAI, and spatial analysis.
Choosing this course at Geo-ICT Training Center offers unique advantages. We combine up-to-date knowledge of modern geospatial data analysis with practical applications within GIS, Geo-ICT, data engineering, and GeoAI workflows. The course is designed to be hands-on, so participants not only learn what the spatial extension is, but more importantly, how they can apply it directly in their own work. Thanks to our focus on GIS, GeoAI, open-source tooling, and modern geodata standards, this course provides a strong foundation for professionals who want to work with geodata in a future-oriented way.
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Contact usDuring this hands-on course, you will be introduced to DuckDB Spatial and learn how to analyze geospatial data using Spatial SQL. You will learn how to import geodata from GeoParquet, GeoJSON, Shapefiles, and GeoPackages, and work with geometries, coordinate systems, and spatial functions. You will then perform spatial analyses such as calculating distances, creating buffers, performing spatial joins, and combining datasets based on location. In addition, you will learn about integrating DuckDB Spatial with Python, R, GeoPandas, and sf. By the end of the course, you will have a solid foundation for applying DuckDB Spatial within GIS, GeoAI, and modern geodata workflows.
Import and manage geospatial datasets from formats such as GeoParquet, GeoJSON, Shapefile, and GeoPackage.
Perform spatial analyses using Spatial SQL, including calculating distances, creating buffers, and analyzing spatial relationships.
Apply spatial joins and geodata combinations to efficiently link geographic and administrative datasets.
Integrate DuckDB into modern geodata workflows with Python, R, GeoPandas, sf, and GeoAI applications.
Do you have questions about the course content? Or are you unsure whether the course aligns with your learning goals or preferences? Would you prefer an in-house or private course? We’d be happy to help.
DuckDB Spatial is designed for fast analytical processing of geodata without requiring a separate database server. PostGIS, on the other hand, is a full-fledged spatial extension for PostgreSQL and is often used in multi-user and production environments. For analyses on files such as GeoParquet, DuckDB often offers a simpler and lighter solution.
Yes. One of DuckDB’s strengths is that GeoParquet files can be queried directly using SQL. This means that geodata does not need to be imported into a database first, which can save a lot of time and storage space.
No. A basic understanding of SQL and GIS is sufficient. The course focuses on performing spatial analyses using SQL. Integrations with Python and R will be discussed, but programming experience is not required.
Absolutely. QGIS users learn how to analyze and prepare large geospatial datasets much more quickly using SQL. The results can then be used within QGIS for visualization, map production, and further GIS analysis.
