Databases
DuckDB is increasingly being used for the lightning-fast analysis of large datasets in data analysis, Geo-ICT, AI, and modern analytics workflows. For example, organizations use DuckDB to process Parquet files, analyze millions of records without a heavy-duty database server, and combine SQL with Python and R environments. Thanks to its high performance and ease of use, DuckDB is rapidly emerging as a popular alternative to traditional analytics databases.
DuckDB is increasingly being used for the rapid analysis of large datasets in data analysis, Geo-ICT, AI, and modern analytics workflows. Organizations use DuckDB, for example, to process Parquet files, analyze millions of records without a heavy-duty database server, and combine SQL with Python and R environments.
DuckDB is a modern, embedded analytics database specifically designed for fast analytical queries. Unlike traditional database servers, DuckDB does not need to be installed or managed as a separate server. This makes DuckDB particularly well-suited for data analysts, GIS specialists, researchers, and developers who want to work directly from files, scripts, or notebooks.
A key advantage of DuckDB is its strong support for modern data formats such as CSV, JSON, and Parquet. Parquet, in particular, is playing an increasingly important role in data engineering, cloud data lakes, GeoAI, and scalable analytics environments. With DuckDB, these files can be queried directly using SQL, without the data first needing to be loaded into a heavy-duty database environment.
DuckDB integrates well with modern workflows using Python, R, Pandas, Polars, and Jupyter notebooks. As a result, DuckDB serves as a practical bridge between traditional SQL analysis and modern data science. Participants will learn how to efficiently analyze, combine, and prepare large datasets for reports, dashboards, models, and further processing.
DuckDB is of interest to anyone working in data analysis, data engineering, or Geo-ICT who wants to handle large datasets faster, more easily, and with greater flexibility. The database combines the power of SQL with the convenience of working from files and programming environments.
Key points when working with DuckDB:
DuckDB is a relational database designed for analytical workloads. While traditional databases often focus on transaction processing, DuckDB excels at performing fast analyses on large amounts of data. This makes DuckDB suitable for applications where datasets need to be explored, cleaned, merged, and analyzed.
DuckDB makes it possible to quickly turn raw data into actionable insights. This makes it a valuable tool for professionals who want to work efficiently with large datasets without immediately setting up a complex database infrastructure.
In this course, participants learn how to use DuckDB for practical data analysis. The focus is on writing clear SQL queries, working with tables and files, and performing analyses on larger datasets.
DuckDB is often used in combination with programming languages and data science environments. In this course, participants learn how DuckDB fits into modern workflows with Python, R, and DataFrames.
Using DuckDB effectively helps professionals work with modern data sources faster and more easily. The course provides a solid foundation for anyone who wants to use DuckDB in data analysis, data engineering, AI, or Geo-ICT.
Choosing our DuckDB Fundamentals course at Geo-ICT Training Center offers unique benefits. We combine up-to-date knowledge of modern data analysis with practical applications within Geo-ICT, data engineering, and analytics workflows. The course is designed to be hands-on, so participants not only learn what DuckDB is, but more importantly, how they can immediately apply DuckDB in their own work. Thanks to our focus on GIS, GeoAI, open-source tooling, and modern data standards, this course provides a strong foundation for professionals who want to work with data in a future-oriented way.
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Contact usOn the first day of the course, participants will gain a thorough understanding of DuckDB as a modern analytics database. The course begins with an overview of DuckDB’s architecture and philosophy, as well as how it differs from traditional databases such as PostgreSQL, MySQL, and SQLite. Participants will learn why DuckDB is particularly well-suited for analytical workloads, embedded applications, and modern data engineering workflows.
Next, the course covers working with SQL within DuckDB. Key topics include selections, filters, joins, aggregations, and combining datasets. Additionally, participants learn how to work directly with CSV, JSON, and Parquet files without complex import processes. Attention is also given to performance, column-oriented processing, and the efficient analysis of larger datasets.
Throughout the day, participants will engage in hands-on work with datasets and learn how DuckDB can be used for data analysis, reporting, and light ETL processes.
On the second day of the course, participants will delve deeper into DuckDB’s analytical capabilities and working with modern data sources. The focus is on efficiently processing larger datasets and performing more complex SQL analyses within a modern analytics environment.
Participants learn how to combine datasets from different sources and how to use DuckDB for filtering, aggregations, joins, and analytical queries on large volumes of data. The course involves extensive work with CSV, JSON, and especially Parquet files. Attention is also given to optimizing queries and understanding DuckDB’s performance with analytical workloads.
In addition, modern data engineering concepts such as columnar storage, embedded analytics, and data lake workflows are explored. Participants discover how DuckDB can work directly on files without heavy database infrastructures and why DuckDB is increasingly being used in analytics, AI, and Geo-ICT environments.
Throughout the day, participants will engage in hands-on work with datasets and perform various analyses. By the end of the course, participants will have a solid foundation to independently use DuckDB for modern data analysis and analytics workflows.
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.
PostgreSQL is primarily designed as a server-based relational database for transaction processing and multi-user environments. DuckDB, on the other hand, focuses on analytical workloads and runs embedded without requiring a separate server installation. This makes DuckDB particularly well-suited for fast analysis of large files and datasets.
DuckDB uses column-oriented processing and vectorized execution. As a result, the database only needs to read the relevant columns, allowing analytical queries to be executed very efficiently. This makes DuckDB particularly well-suited for aggregations, filtering, and analysis on large datasets.
SQLite is primarily designed for lightweight transactional applications and embedded applications. DuckDB has a similarly simple architecture, but is specifically optimized for data analysis and analytical workloads. As a result, DuckDB offers better performance with large datasets and complex analyses.
Parquet is a columnar file format that works efficiently with analytical databases such as DuckDB. DuckDB can query Parquet files directly without complex import processes. This enables large datasets to be analyzed quickly within modern data engineering, AI, and Geo-ICT workflows.
