Apache Sedona Tutorial for Data Engineers: Scalable Spatial Analytics with Spark скачать в хорошем качестве

Apache Sedona Tutorial for Data Engineers: Scalable Spatial Analytics with Spark

Full Course and Certification: https://www.spatiallab.community/join... 🚀 Ready to move beyond desktop GIS? Step into the Spatial Lab: a global community for ambitious geospatial professionals who want to break out of outdated workflows and join the top 5% of the field. 👉 Join Spatial Lab: https://forrest.nyc/spatial-lab/ 🎓 Want structured, career-changing learning? 🚀 Modern GIS Accelerator: https://forrest.nyc/accelerator/ — master Python, Spatial SQL & cloud workflows in 2 weeks 🧭 Career Compass: https://forrest.nyc/career-compass/ — fast, practical steps to land the GIS role you want 🪄 AI Copilot for GIS: https://forrest.nyc/ai-copilot-for-gis/ — learn to integrate AI into your geospatial workflows & boost your productivity 📰 Weekly modern GIS insights: https://forrest.nyc ⚡️ Spots for the next live cohort and mentorship cycle are closing soon, join now to lock in your place and momentum. Add scalable spatial processing to your data engineering stack with Apache Sedona. In this video, you'll get a hands-on walkthrough of how to use Apache Sedona with Spark to bring geospatial capabilities into your pipeline — no GIS background required. You'll learn how to: ✅ Set up Sedona locally using Spark and Python ✅ Perform spatial joins and geometry operations at scale ✅ Work with satellite imagery ✅ Run everything interactively in a Jupyter Notebook ✅ Move the entire project to run in the cloud on Wherobots Why this matters for data engineers: Modern datasets increasingly contain location data, from logistics to mobility to environmental sensors. Apache Sedona gives you the tools to query and process spatial data natively inside Spark, using SQL or PySpark, without switching to GIS-specific tools. If you’ve used PostGIS, GeoPandas, or even Shapely before and hit performance limits, this is your path forward. 0:00:00 Intro 0:01:34 Spark and Sedona for Geospatial Processing 0:04:00 Comparing Sedona, GeoPandas, PostGIS, and DuckDB 0:10:27 Spatial Lakehouse Architecture 0:12:57 Sedona Intro & Course Set Up 0:14:19 Docker Spark and Sedona Install 0:18:06 Local Spark and Java Install 0:24:46 Understanding Spark Set Up 0:26:46 Sedona Basics 0:35:40 Spatial Spark Dataframes (Vector Data) 0:43:41 Raster Imagery in Sedona 0:53:01 Visualizing Geospatial Data in Sedona 0:56:00 Vector Functions in Sedona 1:07:38 Spatial Joins and Relationships in Sedona 1:13:56 Writing Spatial Data with Sedona 1:19:03 K-Nearest Neighbor Spatial Join in Sedona 1:24:45 Raster Functions in Sedona 1:32:01 Map Algebra and NDVI in Sedona 1:37:15 Raster/Vector Join in Sedona (Zonal Statistics) 1:41:53 Geopandas and Rasterio Compatibility with Sedona 1:43:47 Cloud-Native Sedona with Wherobots CONNECT WITH ME 📸 Instagram:   / matt_forrest   💼 LinkedIn:   / mbforr   📧 Newsletter: https://forrest.nyc 🌐 Website: https://forrest.nyc