Naty Clementi

Geospatial analysis relies on raster data — n-dimensional arrays where each cell holds a spatial measurement. The scale of modern remote sensing data makes CPU-based workflows impractical, but raster operations are naturally parallelizable and well suited for GPU acceleration.

As GPU acceleration becomes essential for scaling Python workloads, many developers face new challenges: understanding installation, managing dependencies, and deploying GPU-enabled environments. Even experienced Python users can struggle to integrate GPUs effectively or troubleshoot performance issues.

Until very recently, producing and using reproducible scientific software environments required advanced knowledge and a strict adherence to best practices (e.g. DOI: 10.25080/majora-212e5952-028).

Geospatial analysis often relies on raster data, n‑dimensional arrays where each cell holds a spatial measurement. Many raster operations, such as computing indices, statistical analysis, and classification, are naturally parallelizable and ideal for GPU acceleration.

In this introductory hands-on tutorial, participants will learn how to accelerate their data workflows with [RAPIDS](https://docs.rapids.ai/), an open-source suite of libraries designed to leverage the power of NVIDIA GPUs for end-to-end data pipelines.

In today’s world of ever-growing data and AI, learning about GPUs has become an essential part of software carpentry, professional development and the education curriculum. However, teaching with GPUs can be challenging, from resource accessibility to managing dependencies and varying knowledge levels.

In this introductory hands-on tutorial, participants will learn how to accelerate their data workflows with [RAPIDS](https://rapids.ai/), an open-source suite of libraries designed to leverage the power of [NVIDIA](https://www.nvidia.com/) GPUs for end-to-end data pipelines.

Geospatial data is becoming more present in data workflows today, and plenty of Python tools allow us to work with it. In the past year, a new contender emerged: DuckDB introduced an extension for analyzing geospatial data.

Tabular data is ubiquitous, and pandas has been the de facto tool in Python for analyzing it. However, as data size scales, analysis using pandas may become untenable.

Nos encanta usar Python en nuestro trabajo, pero lamentablemente la base de datos de tu empresa donde ejecutas tus ETL (extracción, transformación y carga) te lo previene.

Dask is a Python library for scaling and parallelizing Python code. It provides familiar, high-level interfaces to extend the SciPy ecosystem to larger-than-memory or distributed environments, as well as lower-level interfaces for parallelizing custom algorithms.